Nevin Manimala

JMIR Ment Health. 2026 Aug 4;13:e88388. doi: 10.2196/88388.

ABSTRACT

BACKGROUND: The COVID-19 pandemic highlighted a critical need for effective population mental health approaches to target the most prevalent disorders (eg, depression) during periods of elevated community distress. The effectiveness of remotely delivered and web-based interventions should be investigated to identify and innovate high-quality models for population mental health service delivery.

OBJECTIVE: The primary objective investigated the effectiveness of adding Mindfulness-Based Cognitive Therapy for Resilience (MBCT-R)-a live, online, synchronous, remotely delivered, group-based intervention-to Cambridge Health Alliance MindWell (CHA-MW), a web-based population health screening and stratified support program, compared with CHA-MW alone, on depression symptom severity. The secondary objective evaluated adding internet Cognitive Behavioral Therapy (iCBT)-an asynchronous, web-based, individual, digital intervention-to CHA-MW, compared with CHA-MW alone.

METHODS: Participants (N=97) were randomized in a 2:2:1 ratio to receive MBCT-R+CHA-MW (n=37), iCBT+CHA-MW (n=41), or CHA-MW alone (n=19) in a 3-arm randomized clinical trial, from May 2021 to September 2022 in an urban public safety net hospital outpatient setting. CHA-MW served as a low-intensity control condition. For the MBCT-R+CHA-MW arm, MBCT-R was an 8-session program mildly adapted from MBCT to address COVID-19-related risks for depression. For the iCBT+CHA-MW arm, iCBT was a 6-session curriculum added to CHA-MW. All study procedures, including regular mental health symptom screenings, were conducted remotely or via a web-based platform. The primary outcome was change in depression symptom severity during the 24-week study period using an intention-to-treat approach that used generalized linear mixed-effects models to evaluate the comparative effectiveness of MBCT-R+CHA-MW vs CHA-MW over time. A secondary analysis compared iCBT+CHA-MW vs CHA-MW on depression severity. Completer analyses were conducted (per-protocol 6+ sessions). The secondary outcome was mental health visit utilization frequency during the study period.

RESULTS: Both MBCT-R+CHA-MW (mean difference -14.1, 95% CI -21.0 to -7.2) and CHA-MW (mean difference -15.2, 95% CI -21.8 to -8.6) had significant reductions in depression symptom severity, with no statistically significant between-group differences. iCBT+CHA-MW (mean difference -12.7, 95% CI -17.4 to -8.1) also reduced depression symptoms but without between-group differences when compared with CHA-MW. Intervention completion rates were low (MBCT-R: 30% and iCBT: 24%), and completers demonstrated significantly greater reductions in depression severity than noncompleters (mean difference -8.5, 95% CI -16.2 to -0.8). Overall mental health clinician visits by group had no statistically significant differences. CHA-MW had the largest increase in participants with new psychopharmacology treatment visits during the 24-week study (CHA-MW +21%, MBCT-R +10%, and iCBT -5%).

CONCLUSIONS: MBCT-R+CHA-MW, iCBT+CHA-MW, and CHA-MW were each effective in treating depression, without any intervention demonstrating superiority in intention-to-treat analyses. CHA-MW was as efficacious during the COVID-19 pandemic as more resource-intensive interventions that demanded greater time and effort from participants. Low completion rates for MBCT-R and iCBT during the COVID-19 pandemic may have contributed to these results.

PMID:42551008 | DOI:10.2196/88388

Neurology. 2026 Aug 25;107(4):e218406. doi: 10.1212/WNL.0000000000218406. Epub 2026 Aug 4.

ABSTRACT

BACKGROUND AND OBJECTIVES: Enlarged perivascular spaces (EPVSs) in the basal ganglia (BG-EPVS) are an important marker of cerebral small vessel disease (cSVD), and EPVS in the centrum semiovale (CSO-EPVS) are part of the diagnostic criteria for cerebral amyloid angiopathy. We aimed to investigate associations of EPVS with reduced estimated glomerular filtration rate (eGFR) and glomerular hyperfiltration (higher than normal eGFR), which have scarcely been studied previously.

METHODS: In this cross-sectional study, we used pooled individual patient data from the Microbleeds International Collaborative Network which includes patients with ischemic stroke or transient ischemic attack. We investigated associations of impaired kidney function, defined as an eGFR of 30-60 or <30 mL/minute/1.73 m², and glomerular hyperfiltration, defined as eGFR above the age-adjusted and sex-adjusted 95th centile, with BG-EPVS and CSO-EPVS severity. EPVS were rated according to a validated 5-point ordinal scale, and combined cSVD burden was rated using a validated 5-point ordinal scale with 1 point assigned for the presence of each of the following: severe white matter hyperintensities, ≥1 cerebral microbleed, ≥1 lacune, and BG-EPVS ≥11. Normal glomerular filtration was defined as eGFR ≥60 without hyperfiltration. We used multivariable ordinal logistic regression models to estimate risk of increased EPVS and cSVD burden severity adjusted for age, sex, and comorbidities.

RESULTS: Seven thousand two hundred fifty-four patients (mean age 71 ± 13 years, 43% female) were included in the analysis, 357 with glomerular hyperfiltration, 1,692 with eGFR 30-60, and 256 with eGFR <30. Compared with normal glomerular filtration, hyperfiltration was independently associated with BG-EPVS (adjusted odds ratio [aOR] 1.38, 95% CI 1.11-1.70, p < 0.001) and CSO-EPVS (aOR 1.34, 95% CI 1.08-1.64, p = 0.011). Associations of eGFR 30-60 and eGFR <30 with EPVS were not statistically significant. Compared with normal glomerular filtration, eGFR <30 (aOR 1.27, 95% CI 1.03-1.57) was independently associated with increased cSVD burden, but eGFR 30-60 (aOR 1.06, 95% CI 0.95-1.20) and hyperfiltration (aOR 1.15, 95% CI 0.98-1.34) were not.

DISCUSSION: Glomerular hyperfiltration was independently associated with EPVS severity, in both the basal ganglia and centrum semiovale. eGFR <30 was independently associated with total cSVD burden. A key limitation was a lack of repeated eGFR measurements.

PMID:42551001 | DOI:10.1212/WNL.0000000000218406

JMIR AI. 2026 Aug 4;5:e88581. doi: 10.2196/88581.

ABSTRACT

BACKGROUND: Colorectal cancer is a leading cause of cancer-related deaths in the United States, and colonoscopy remains the gold standard for early detection and prevention. However, many procedures are postponed due to inadequate bowel preparation, a preventable failure often caused by patients’ difficulty in understanding and following written prep instructions. Prior interventions such as reminder apps and instructional videos have improved adherence only modestly, largely because they cannot answer patient-specific questions. Recent advances in large language models (LLMs) raise the possibility of developing conversational assistants that can provide interactive support to patients in procedure preparation.

OBJECTIVE: This study evaluated the correctness, harmfulness, and diversity of synthetic dialogues generated by leading LLMs acting as both simulated AI Coaches and patients for colonoscopy preparation.

METHODS: Five leading LLMs-OpenAI’s o3, GPT-4.1, and GPT-5.1; Meta’s Llama 3.3 70B; and Mistral’s Large-2411-were used to generate 250 patient-AI Coach dialogues per model. Dialogues consisted of 3 to 7 question-answer pairs concerning diet, medications, and other prep-related topics. A multiprompt, multiquestion approach was designed to elicit diverse patient questions, and an error taxonomy was established to assess model capabilities in responding to questions. Human raters, including 3 medical experts, evaluated the generated questions for difficulty and the responses for correctness, error type, and potential harmfulness. Automatic evaluation using an LLM-as-a-judge approach complemented human evaluation. Question diversity was assessed using lexical diversity metrics (Distinct-1 and Distinct-2) and entropy. In addition, we evaluated a safety filtering mechanism in which responses judged incorrect by an automated evaluator were replaced with a deferral message instructing patients to contact their health care provider. Differences in response correctness across models were evaluated using permutation tests conducted at the dialogue level. Interrater agreement among human evaluators was assessed using the Gwet AC1 statistic. The study was conducted between May and September 2025.

RESULTS: Automatic evaluation results closely aligned with human judgments: leading models approached but did not achieve adequate performance. Closed-weight models (GPT-5.1, GPT-4.1, and o3) outperformed open-weight models (Llama and Mistral) on correctness, with the reasoning models (GPT-5.1 and o3) performing best. This turn-level ranking was preserved under the supplementary single-prompt baseline, although dialogue-level rankings differed. All models produced harmful errors, primarily due to omissions or misinterpretations of prep instructions. The multiprompt generation strategy substantially increased the diversity of patient questions compared with a single-prompt baseline. Applying an automated safety filter reduced overall error rates but failed to eliminate harmful responses.

CONCLUSIONS: Although LLMs demonstrate strong potential to support colonoscopy preparation, none are yet reliable enough for unsupervised deployment in patient-facing contexts. Persistent harmful errors and the limited effectiveness of simple filtering mechanisms highlight the need for improved instruction adherence, stronger safety mechanisms, and validation using real patient queries.

PMID:42550998 | DOI:10.2196/88581

JMIR Form Res. 2026 Aug 4;10:e84836. doi: 10.2196/84836.

ABSTRACT

BACKGROUND: Continued cannabis use among young people with first-episode psychosis (FEP) has been linked to poorer clinical and functional outcomes (eg, increased symptom severity and higher relapse rates). Digital harm-reduction interventions may represent a promising, person-centered approach to reduce at-risk cannabis use behaviors in this population. However, evidence remains limited regarding which subgroups are more likely to engage with these interventions and whether specific levels of engagement are required to achieve more favorable cannabis-related outcomes.

OBJECTIVE: This exploratory, hypothesis-generating analysis of the CHAMPS (Cannabis Harm-Reducing App to Manage Practices Safely) pilot randomized controlled trial (RCT) evaluated a cannabis harm-reduction mobile app for youth with FEP in early intervention services (EIS). The objectives of this study were to assess engagement by examining associations between selected sociodemographic factors and module completion and to determine whether achieving specific completion thresholds was associated with improvements in cannabis-related outcomes.

METHODS: Cannabis-related outcomes (Marijuana Problems Scale [MPS], Protective Behavioral Strategies for Marijuana [PBSM] scores, and days of cannabis use) were self-assessed at baseline and at week 6 (primary end point), week 12, and week 18 (postrandomization). Participants were categorized into low to moderate (0-4 modules) and high (5-6 modules) completion groups, and selected sociodemographic factors were compared between groups using bivariate analyses. Mixed-effects models, adjusted for baseline values and the covariates sex and cannabis use disorder (CUD) status, were fitted to evaluate whether module thresholds were associated with improvements in cannabis-related outcomes.

RESULTS: Data from 96 participants, including 46 in the CHAMPS+EIS arm and 50 in the EIS-only arm (1:1 ratio), were analyzed under a modified intention-to-treat principle. Individuals in the high-engagement group reported higher baseline social support and higher educational level (P=.005 and P=.01). No statistically significant associations were observed between specific module completion thresholds and cannabis-related outcomes in adjusted mixed-effects models.

CONCLUSIONS: Participants with higher social support and higher education were more likely to engage with CHAMPS. Although descriptive analyses suggested a potential gradient of improvement with increasing module completion, no specific completion threshold was robustly associated with improvements in outcomes. Given the multifactorial nature of engagement, supporting subgroups at risk of lower app use and examining metrics beyond module completion may enhance the impact of CHAMPS. These findings are hypothesis-generating, given their exploratory nature, and require replication in a future efficacy trial.

PMID:42550986 | DOI:10.2196/84836

JMIR Med Inform. 2026 Aug 4;14:e83889. doi: 10.2196/83889.

ABSTRACT

BACKGROUND: Transfusion thresholds in upper gastrointestinal bleeding are debated; hemoglobin cutoffs of 70-80 g/L are widely cited yet inconsistently applied. Common risk scores offer limited individualized guidance and rarely provide calibrated, interpretable predictions for transfusion decisions.

OBJECTIVE: This study aimed to develop and validate a two-stage, clinically constrained gradient-boosting framework (Medically Constrained Gradient Boosting [MCGB]) that predicts transfusion need and estimates transfusion dose with quantified uncertainty and to implement a prototype recommendation system for clinical use.

METHODS: We analyzed a retrospective multicenter cohort of 849 adults with endoscopically confirmed upper gastrointestinal bleeding admitted to 3 hospitals in Chongqing, China (January 2019 to August 2025). Predictors available before the transfusion decision included demographics, first recorded vital signs, initial laboratory indices, and clinician-adjudicated etiology. Stage 1 used a calibrated classifier with prespecified monotonic constraints and stability-screened, clinically justified interactions. Stage 2 modeled transfusion dose via quantile predictions with conformal adjustment to generate 95% prediction intervals. Performance was assessed using a cross-site hold-out design. Overall, 2 hospitals were used as the development cohort, within which stratified 5-fold cross-validation was performed for model development, hyperparameter tuning, interaction screening, and calibration. The remaining hospital was held out as an independent test cohort for final evaluation. Hospital-wise alternating external testing was further conducted as a supplementary robustness analysis to assess performance stability across institutions. Classification performance was evaluated using discrimination metrics (area under the receiver operating characteristic curve and area under the precision-recall curve), calibration metrics, and decision-curve analysis; regression performance was evaluated using R², mean absolute error, and prediction-interval coverage. A graphical user interface was implemented to enable clinicians to input patient data and obtain calibrated predictions of transfusion probability and corresponding dose recommendations.

RESULTS: MCGB achieved strong discrimination and good calibration across subgroups (area under the receiver operating characteristic curve=0.97 and area under the precision-recall curve=0.91). At a reference probability threshold of .50, sensitivity, specificity, and F1-scores were 0.99, 0.87, and 0.85, respectively, providing a representative operating point for comparison. For dose prediction among transfused patients, MCGB achieved R² of 0.95 and mean absolute error 0.04; 95% prediction-interval coverage was 0.94, indicating accurate point estimates with reliable uncertainty quantification. The software prototype further demonstrated feasibility of real-time decision support at the bedside.

CONCLUSIONS: MCGB provides calibrated, interpretable predictions of transfusion need and individualized dose in upper gastrointestinal bleeding and may support bedside decision-making and blood-bank planning, with a prototype interface demonstrating potential for clinical deployment. External validation in additional settings is warranted to confirm generalizability.

PMID:42550984 | DOI:10.2196/83889

JMIR Med Inform. 2026 Aug 4;14:e98207. doi: 10.2196/98207.

ABSTRACT

BACKGROUND: Pneumonia is a common infectious disease, and antibiotic treatment in hospitalized patients must balance efficacy, safety, and resistance risk. However, antibiotic selection and dose adjustment still rely heavily on clinician experience. Although large language models (LLMs) are promising for clinical reasoning, their direct use for antibiotic selection and dose recommendation is limited by hallucinations and weak adherence to clinical constraints.

OBJECTIVE: This study aimed to develop and externally validate a constrained LLM-based clinical decision support pipeline for antibiotic selection and dose recommendation in hospitalized patients with pneumonia.

METHODS: We conducted a multicenter retrospective study using electronic health record narratives, antibiotic orders, and laboratory indicators of hepatic and renal function from 331 hospitalized patients with pneumonia from 2 hospitals in China. The development cohort included 233 patients, and the external validation cohort included 98 patients. The pipeline integrated dual-branch retrieval (similar-case vector retrieval plus guideline-based knowledge graph retrieval), clinician-defined rule constraints, and hybrid-context reasoning. DeepSeek-V3, GLM-4.6, and GPT-4o were evaluated using F1-score and Jaccard accuracy.

RESULTS: On the internal test set, the full pipeline using DeepSeek-V3 achieved the best performance, with an F1-score of 0.8110 (95% CI 0.7371-0.8762) and Jaccard accuracy of 0.7624 (95% CI 0.6810-0.8386) for antibiotic selection and an F1-score of 0.7538 (95% CI 0.6671-0.8329) and Jaccard accuracy of 0.7076 (95% CI 0.6145-0.7938) for joint antibiotic selection plus dosing recommendation. On the external validation set, performance remained high, with an F1-score of 0.8605 (95% CI 0.7891-0.9252) and Jaccard accuracy of 0.8571 (95% CI 0.7857-0.9184) for antibiotic selection, and an F1-score of 0.8503 (95% CI 0.7789-0.9150) and Jaccard accuracy of 0.8469 (95% CI 0.7755-0.9133) for antibiotic selection plus dosing recommendation. The system also provided traceable evidence and rule trigger information to support clinician review.

CONCLUSIONS: A constrained, retrieval-augmented LLM pipeline improved the consistency and interpretability of antibiotic selection and dose recommendation for hospitalized patients with pneumonia and provided preliminary evidence of cross-site generalizability.

PMID:42550965 | DOI:10.2196/98207

J Gastrointestin Liver Dis. 2026 Jul 20. doi: 10.15403/jgld-7495. Online ahead of print.

ABSTRACT

BACKGROUND AND AIMS: Inflammatory bowel disease (IBD) requires reliable non-invasive biomarkers to monitor mucosal healing, systemic inflammation, and gut-liver axis involvement. This study evaluated the diagnostic potential of accessible clinical parameters, including fecal calprotectin (FC), hemogram-derived ratios, and composite albumin-integrating and liver fibrosis scores, alongside standardized endoscopic severity findings.

METHODS: This retrospective observational study included 36 adult IBD patients [21 with Crohn’s disease (CD), 15 with ulcerative colitis (UC) receiving biological therapy. Systemic inflammatory indices [e.g., neutrophil-lymphocyte ratio (NLR)], nutritional scores [e.g. prognostic nutritional index (PNI)], and hepatic fibrosis scores [e.g. aspartate aminotransferase to platelet ratio index (APRI), fibrosis 4 (FIB-4), platelet-albumin-bilirubin (PALBI)] were assessed. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic performance, reported as area under the curve (AUC), of these biomarkers.

RESULTS: Routine laboratory parameters and non-invasive liver fibrosis indices showed no statistically significant differences between CD and UC patients. Biologic treatment initiation differed significantly between the groups (p<0.001). Endoscopically, CD presented marked phenotypic heterogeneity with predominantly ileocolonic involvement, whereas active UC was characterized mainly by pancolitis. Fecal calprotectin concentrations were notably higher in UC (median 1000 μg/g) compared to CD (310 μg/g). Diagnostically, FC demonstrated the highest predictive capacity for differentiating the conditions (AUC=0.74). Furthermore, albumin-integrating scores, specifically the PALBI score (AUC = 0.686) and PNI (AUC = 0.657), alongside NLR (AUC=0.632), outperformed traditional hepatic indices in capturing the systemic inflammatory toll. Complex composite inflammatory formulas unexpectedly underperformed.

CONCLUSIONS: FC remains the most robust non-invasive marker for localized mucosal inflammation. Albumin-integrating scores and simple hemogram-derived ratios better reflect the systemic inflammatory burden than pure hepatic fibrosis indices, but their moderate diagnostic accuracies preclude independent clinical application. Effective IBD management requires integrating these non-invasive adjunctive tools with standard endoscopic assessments within a personalized, multiparametric framework.

PMID:42550963 | DOI:10.15403/jgld-7495

JMIR AI. 2026 Aug 4;5:e78485. doi: 10.2196/78485.

ABSTRACT

BACKGROUND: Patient-reported outcome measures (PROMs) are central to multinational clinical research, but high-quality translation and linguistic validation remain resource-intensive. AI-powered translation may accelerate this process, but its performance relative to validated human PROM translations requires systematic evaluation.

OBJECTIVE: This benchmarking study evaluated the quality and comparability of 4 AI-powered translation services for the EuroQol 5-dimension 5-level (EQ-5D-5L) across 5 target languages, using official, linguistically validated human translations as the reference standard (gold standard).

METHODS: The 43 text segments of the EQ-5D-5L were translated from English into Danish, Dutch, French, German, and Spanish using Google Translate, GPT-4.1, Amazon Translate, and DeepL. GPT-4.1 was evaluated with a structured medical-translator prompt, whereas Google Translate, Amazon Translate, and DeepL were evaluated using standard unprompted application programming interfaces without domain-specific glossary constraints. Outputs were benchmarked against official, validated human translations using 4 automated metrics: BLEU (bilingual evaluation understudy), METEOR (metric for evaluation of translation with explicit ordering), COMET (cross-lingual optimized metric for evaluation of translation), and BLEURT (bilingual evaluation understudy with representations from transformers). Friedman tests were used to assess overall between-service differences within each metric-language combination. When the Friedman test was significant, paired Wilcoxon signed-rank post hoc tests with Holm-Bonferroni correction were conducted. Descriptive summaries, score distributions, and sentence-level hotspot analyses were used to evaluate semantic similarity patterns and identify localized low-scoring deviations.

RESULTS: Friedman tests assessed whether the AI services differed in performance, whereas descriptive summaries and visualizations were used to determine whether scores clustered in ranges consistent with strong semantic similarity to the gold standard. Friedman tests identified statistically significant between-service differences in 11 of the 20 (55%; P<.05) metric-language combinations. Subsequent paired Wilcoxon signed-rank post hoc tests with Holm-Bonferroni correction identified 11 significant pairwise differences, with adjusted P values ranging from <.001 to .049. Most of these differences were detected by surface-overlap metrics (10/11 for BLEU or METEOR), whereas only 1 of 11 was detected by a semantic metric (BLEURT), suggesting that many between-service differences were stylistic rather than meaning-altering. Descriptive and visual analyses further showed that semantic similarity was generally high across services, while low-scoring deviations clustered in specific linguistic hotspots, particularly domain headers, abstract health concepts, and short, context-dependent interface strings.

CONCLUSIONS: Among the evaluated high-resource European languages, AI translation services showed high semantic similarity to the validated human translations, although localized conceptual deviations persisted. These findings suggest that AI can support the generation of translations for PROM workflows in these languages; however, expert human review may still be required to confirm conceptual equivalence. The practical relevance of isolated header differences could not be assessed in the present study, whereas abstract health concepts and other clinically sensitive phrasing should be evaluated in further research.

PMID:42550950 | DOI:10.2196/78485

Am J Speech Lang Pathol. 2026 Aug 4:1-24. doi: 10.1044/2026_AJSLP-25-00321. Online ahead of print.

ABSTRACT

PURPOSE: This study examined whether systemic and experiential factors predict overall job satisfaction among speech-language pathology assistants (SLPAs) and supervising speech-language pathologists (SLPs). Specifically, the study explored the influence of state regulation category, years of experience, and supervision practices on satisfaction in both roles.

METHOD: Participants included 68 professionals (38 SLPAs, 30 SLPs) recruited through professional networks and social media. Ordinal logistic regression was used to identify predictors of satisfaction. SLPA satisfaction was analyzed in relation to years of experience, state regulation category, and monthly hours of direct supervision. SLP satisfaction was examined in relation to state regulation category and perceptions of SLPA impact on workload and caseload.

RESULTS: Among SLPAs, years of experience significantly predicted overall job satisfaction, with greater experience associated with higher odds of satisfaction. Hours of direct supervision per month did not significantly predict overall satisfaction but were positively associated with satisfaction regarding the quantity of supervision received. Supervisory practices varied in frequency, with limited opportunities reported for observation and feedback on therapy material selection. Among SLPs, no statistically significant predictors of job satisfaction were identified. Perceptions of SLPA impact on workload and caseload were divided.

CONCLUSIONS: Findings highlight the role of experience and supervision in shaping SLPA satisfaction, particularly during early career stages. Although supervision time was not linked to overall satisfaction, it was associated with perceptions of supervisory adequacy. For supervising SLPs, satisfaction may be influenced by factors beyond those examined in this study. Implications for training, supervision practices, and systemic support are discussed. Given the modest effect sizes and small sample, these findings should be interpreted as preliminary and exploratory.

PMID:42550947 | DOI:10.1044/2026_AJSLP-25-00321

South Med J. 2026 Aug 3;119(8):500-503. doi: 10.14423/SMJ.0000000000001996.

ABSTRACT

OBJECTIVES: Using the most recent US population projections data, we sought to update the estimated number of women who will undergo surgery for stress urinary incontinence (SUI) and pelvic organ prolapse (POP) in the United States from 2025 through 2060. We hypothesize that the number of pelvic floor surgeries will increase in the upcoming decades.

METHODS: We used the 2017 National Population Projections from the US Census Bureau, which provides age-specific estimates on the number of women in the US from 2025 to 2060. We used previously published age-specific rates of surgery for women undergoing SUI-only surgery, POP-only surgery, and either SUI or POP surgery. These rates were applied to the population estimates of women aged 18 to 89 years to determine the projected surgeries from 2025 to 2060 in 5-year increments.

RESULTS: From 2025 to 2060, the population of women in the United Sates ages 18 to 89 years is projected to increase 17%, from 136.0 million to 158.5 million. Correspondingly, the total number of either SUI or POP surgeries will increase from 469,460 in 2025 to 553,858 in 2060.

CONCLUSIONS: From 2025 to 2060, there will be an 18% increase in the projected number of surgeries for SUI or POP, from 469,460 to 553,858. Our field should be proactive in ensuring that enough specialists and fellowship-trained subspecialists are available to meet the future surgical demands of women with pelvic floor disorders.

PMID:42550940 | DOI:10.14423/SMJ.0000000000001996