Tag: nevin manimala

N Z Med J. 2025 May 2;138(1614):81-90. doi: 10.26635/6965.6844.

ABSTRACT

AIM: Ethnicity and descent are two different officially recognised identity definitions for the Indigenous Māori population of New Zealand. Official health statistics are usually reported by Māori ethnicity but not descent, as health collections such as the New Zealand Cancer Registry (NZCR) do not record Māori descent information. We explored the potential of linked administrative data to describe health outcomes by Māori descent using gastric (stomach) cancer as an example.

METHODS: The Integrated Data Infrastructure (IDI) was used to source information on Māori descent from the 2013 and 2018 censuses as well as birth and death records linked to the NZCR for gastric cancer registrations for the years 1995-2021 (N=10,575).

RESULTS: Māori descent information could be sourced for 81.8% of gastric cancer registrations. Descent information was available for 65.2% of gastric cancer registrations in death records, 39.5% in the 2013 or 2018 census, 6.1% from a child’s birth record and ≤0.3% from personal birth records. Of the registrations for whom Māori descent information could be obtained, 18.6% were identified as being of Māori descent vs 17.3% identified as Māori by ethnicity. Missing Māori descent data was lower (around 5%) in more recent gastric cancer registrations (2012 onwards).

CONCLUSION: Based on our case study, classifying cancer registrations by Māori descent for health outcome reporting, in addition to Māori ethnicity, may be feasible for recent years of data. Use of death records for Māori descent information should be carefully considered, as this may introduce bias to analyses such as survival analysis.

PMID:40311134 | DOI:10.26635/6965.6844

JMIR Hum Factors. 2025 May 1;12:e66668. doi: 10.2196/66668.

ABSTRACT

BACKGROUND: Latino adults with type 2 diabetes (T2D) have higher rates of diabetes medication nonadherence than non-Hispanic White adults. REACH (Rapid Encouragement/Education And Communications for Health) is a text message platform based on the information-motivation-behavioral skills model that addresses barriers to adherence and was shown to improve adherence and glycated hemoglobin (HbA1c) levels, but it is only available in English.

OBJECTIVE: This study aimed to report the multiphase, stakeholder-driven adaptation of the REACH barriers to diabetes medication adherence content to a Latino population (REACH-Español).

METHODS: This was a qualitative study using focus groups. We identified potentially eligible patients (≥18 y old, Latino ethnicity, Spanish-language preference, and T2D diagnosis) using a Mass General Brigham Hospital query. Eligible patients were invited to participate in a focus group conducted in Spanish between April 13 and November 9, 2023. A total of 5 focus groups were conducted. Focus groups 1-3 centered on ranking 40 barriers to diabetes medication adherence (derived from REACH and the extant literature), whereas focus groups 4-5 centered on translation and cultural modifications of the original SMS text message content associated with each of the REACH barriers. Barriers were mapped onto information-motivation-behavioral constructs. We used descriptive statistics to summarize participant characteristics. Focus groups were audio-recorded, professionally transcribed, and analyzed with thematic content analysis using NVivo (Lumivero).

RESULTS: In total, 22 participants attended the focus groups. The mean (SD) age was 63.2 (11) years, 55% (n=10/22) were female, and the mean HbA1c level was 8.5%. All participants were born in Latin America or the Caribbean and spoke Spanish as their preferred language, and 54.5% (12/22) had completed middle-school education or less. Among the top 10 ranked barriers, 50% (n=5) corresponded to information, 20% (n=2) to social motivation, 20% (n=2) to behavioral skills, and 10% (n=1) to personal motivation. Personal motivation barriers (medication burden and fear of side effects) and behavioral skills (forgetting to take medication) emerged as important themes in the focus groups.

CONCLUSIONS: A stakeholder-driven approach to intervention adaptation identified and prioritized relevant barriers to diabetes medication adherence among Latino adults with T2D and facilitated the adaptation of the REACH platform to a Spanish-speaking population.

PMID:40311126 | DOI:10.2196/66668

JMIR Med Educ. 2025 May 1;11:e66186. doi: 10.2196/66186.

ABSTRACT

BACKGROUND: Approximately 4000 preventable surgical errors occur per year in the US operating rooms, many due to suboptimal teamwork and safety behaviors. Such errors can result in temporary or permanent harm to patients, including physical injury, emotional distress, or even death, and can also adversely affect care providers, often referred to as the “second victim.”

OBJECTIVE: Given the persistence of adverse events in the operating rooms, the objective of this study was to quantify the effect of an innovative and immersive virtual reality (VR)-based educational intervention on (1) safety behaviors of surgeons in the operating rooms and (2) sense-making regarding the overall training experience.

METHODS: This mixed methods pre- versus postintervention pilot study was conducted in a large academic medical center with 55 operating rooms. Safety behaviors were observed and quantified using validated Teamwork Evaluation of Non-Technical Skills instrument during surgical cases at baseline (101 observations; 83 surgeons) and postimmersive VR based intervention (postintervention: 24 observations within each group; intervention group [with VR training; 10 surgeons] and control [no VR training; 10 surgeons]). VR intervention included a 45-minute immersive VR-based training incorporating a pre- and postdebriefing based on Team Strategies and Tools to Enhance Performance and Patient Safety (TeamSTEPPS) principles to improve safety behaviors. A 2-tailed, 2-sample t-test with adjustments for multiplicity of the tests was used to test for significance in observable safety behaviors between the groupings. The debriefing data underwent analysis through the phenomenological analysis method to gain insights into how participants interpreted the training.

RESULTS: Preintervention, all safety behaviors averaged slightly above “acceptable” scores, with an overall average of 2.2 (range 2-2.3; 0-3 scale). The 10 surgeons that underwent our intervention showed statistically significant (P<.05) improvements in 90% (18/20) of safety behaviors when compared to the 10 surgeons that did not receive the intervention (overall average 2.5, range 2.3-2.7 vs overall average 2.1, range 1.9-2.2). Our qualitative analysis based on 492 quotes from participants suggests that the observed behavioral changes are a result of an immersive experience and sense-making of key TeamSTEPPS training concepts.

CONCLUSIONS: VR-based immersive training intervention focused on TeamSTEPPS principles seems effective in improving safety behaviors in the operating rooms as quantified via observations using the Teamwork Evaluation of Non-Technical Skills instrument. Further research with larger, more diverse sample sizes is needed to confirm the generalizability of these findings.

PMID:40311122 | DOI:10.2196/66186

JMIR Form Res. 2025 May 1;9:e66189. doi: 10.2196/66189.

ABSTRACT

BACKGROUND: Postoperative bleeding is a serious complication following abdominal tumor surgery, but it is often not clearly diagnosed and documented in clinical practice in China. Previous studies have relied on manual interpretation of medical records to determine the presence of postoperative bleeding in patients, which is time-consuming and laborious. More critically, this manual approach severely hinders the efficient analysis of large volumes of medical data, impeding in-depth research into the incidence patterns and risk factors of postoperative bleeding. It remains unclear whether machine learning can play a role in processing large volumes of medical text to identify postoperative bleeding effectively.

OBJECTIVE: This study aimed to develop a machine learning model tool for identifying postoperative patients with major bleeding based on the electronic medical record system.

METHODS: This study used data from the available information in the National Health and Medical Big Data (Eastern) Center in Jiangsu Province of China. We randomly selected the medical records of 2,000 patients who underwent in-hospital tumor resection surgery between January 2018 and December 2021 from the database. Physicians manually classified each note as present or absent for a major bleeding event during the postoperative hospital stay. Feature engineering involved bleeding expressions, high-frequency related expressions, and quantitative logical judgment, resulting in 270 features. Logistic regression (LR), K-nearest neighbor (KNN), and convolutional neural network (CNN) models were developed and trained using the 1600-note training set. The main outcomes were accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) for each model.

RESULTS: Major bleeding was present in 4.31% (69/1600) of the training set and 4.75% (19/400) of the test set. In the test set, the LR method achieved an accuracy of 0.8275, a sensitivity of 0.8947, a specificity of 0.8241, a PPV of 0.2024, an NPV of 0.9937, and an F1-score of 0.3301. The CNN method demonstrated an accuracy of 0.8900, sensitivity of 0.8421, specificity of 0.8924, PPV of 0.2807, NPV of 0.9913, and an F1-score of 0.4211. While the KNN method showed a high specificity of 0.9948 and an accuracy of 0.9575 in the test set, its sensitivity was notably low at 0.2105. The C-statistic for the LR method was 0.9018 and for the CNN method was 0.8830.

CONCLUSIONS: Both the LR and CNN methods demonstrate good performance in identifying major bleeding in patients with postoperative malignant tumors from electronic medical records, exhibiting high sensitivity and specificity. Given the higher sensitivity of the LR method (89.47%) and the higher specificity of the CNN method (89.24%) in the test set, both models hold promise for practical application, depending on specific clinical priorities.

PMID:40311117 | DOI:10.2196/66189

Blood Adv. 2025 May 1:bloodadvances.2025015834. doi: 10.1182/bloodadvances.2025015834. Online ahead of print.

ABSTRACT

Anti-CD19 chimeric antigen receptor (CAR) T cells have shown impressive results in the treatment of relapsed/refractory aggressive large B-cell lymphomas (LBCLs). However, the prognostic value of the LBCL histological subtype in the context of CAR T cell therapy is unclear. Here, we report the prognostic value of transformed indolent non-Hodgkin lymphoma (TriNHL) (n=110) confirmed by an expert pathologic review (LYMPHOPATH) vs. de novo LBCL (n=391) in the context of CAR T cell therapy from 4 centers of the French DESCAR-T registry. After 1:1 propensity score matching (N=170, 85 TriNHL patients and 85 de novo LBCL patients), the median follow-up was 19.4 months (95% CI, 12.0-25.1) for TriNHL patients and 18.5 months (95% CI, 13.8-24.8) for LBCL patients. The 1-year progression-free survival rate was significantly better (55.8%, [95% CI, 43.6-66.4]) in the TriNHL group than in the de novo LBCL group (31.7%, [95% CI, 21.4-42.6]) (hazard ratio=0.54, [95% CI, 0.36-0.82], p=0.0034). The best overall response rate/complete response rate was 82.4%/63.5%, whereas it was 63.5%/50.6% for the TriNHL group compared with the de novo LBCL group. The 1-year overall survival was also longer in the TriNHL group than in the de novo LBCL group (72.1%, [95% CI, 59.6-81.4] vs. 50.7%, [95% CI, 38.2-62.0], p=0.031). Similar findings were found via an inverse probability weighting statistical approach. No difference was observed in terms of toxicity. In conclusion, our matched-comparison study revealed a greater efficacy of CAR T cell therapy, with a comparable toxicity profile for TriNHL patients than for LBCL patients.

PMID:40311067 | DOI:10.1182/bloodadvances.2025015834

Science. 2025 May;388(6746):532-537. doi: 10.1126/science.adn4381. Epub 2025 May 1.

ABSTRACT

Efforts to address declines of North American birds have been constrained by limited availability of fine-scale information about population change. By using participatory science data from eBird, we estimated continental population change and relative abundance at 27-kilometer resolution for 495 bird species from 2007 to 2021. Results revealed high and previously undetected spatial heterogeneity in trends; although 75% of species were declining, 97% of species showed separate areas of significantly increasing and decreasing populations. Populations tended to decline most steeply in strongholds where species were most abundant, yet they fared better where species were least abundant. These high-resolution trends improve our ability to understand population dynamics, prioritize recovery efforts, and guide conservation at a time when action is urgently needed.

PMID:40310906 | DOI:10.1126/science.adn4381

PLoS Med. 2025 May 1;22(5):e1004592. doi: 10.1371/journal.pmed.1004592. eCollection 2025 May.

ABSTRACT

BACKGROUND: Health data disaggregated by sex is vital for identifying the distribution of illness, and assessing risk exposures, service access, and utilization. Disaggregating data along a health pathway, i.e., the measurable continuum from risk factor exposure to final health outcome (death), and including disease prevalence and a three-step care cascade (diagnosis, treatment, and control), has the potential to provide a holistic and systematic source of information on sex- and gender-based health inequities and identify opportunities for more tailored interventions to reduce those inequities.

METHODS AND FINDINGS: We collected sex- and age-disaggregated data along the health pathway. We searched for papers using global datasets on the sex-disaggregated care cascade for eight major conditions and identified cascade data for only three conditions: hypertension, diabetes, and HIV and AIDS. For each condition, we collected risk factor prevalence, disease prevalence, cascade progression, and death rates. We assessed the sex difference for all steps along the pathway and interpreted inequities through a lens of gender analysis. Sex-disaggregated data on risk factors, disease prevalence, and mortality were found for all three conditions across 204 countries. Sex-disaggregated care cascades for hypertension, diabetes, and HIV and AIDS were found only for 200, 39, and 76 countries, respectively. Significant sex differences were found in each step along the pathways. In many countries, males exhibited higher disease prevalence and death rates than females, while in some countries, they also reported lower rates of healthcare seeking, diagnosis, and treatment adherence. Smoking prevalence was higher among males in most countries, whereas prevalence of obesity and unsafe sex were higher in females in most countries.

CONCLUSIONS: Findings support the increasing need to develop strategies that encourage greater male participation in preventive and healthcare service and underscore the importance of sex-disaggregated data in understanding health inequities and guiding gender-responsive interventions at different points along the pathway. Despite limitations in data availability and completeness, this study elucidates the need for more comprehensive and harmonized datasets for these and other conditions to monitor sex differences and implement sex-/gender-responsive interventions along the health pathway.

PMID:40310879 | DOI:10.1371/journal.pmed.1004592

J Frailty Aging. 2025 Apr 29;14(3):100049. doi: 10.1016/j.tjfa.2025.100049. Online ahead of print.

NO ABSTRACT

PMID:40310717 | DOI:10.1016/j.tjfa.2025.100049

Chaos. 2025 May 1;35(5):053101. doi: 10.1063/5.0244221.

ABSTRACT

A transformer-based Koopman autoencoder is proposed for linearizing Fisher’s reaction-diffusion equation. The primary focus of this study is on using deep learning techniques to find complex spatiotemporal patterns in the reaction-diffusion system. The emphasis is on not just solving the equation but also transforming the system’s dynamics into a more comprehensible, linear form. Global coordinate transformations are achieved through the autoencoder, which learns to capture the underlying dynamics by training on a data set with 60,000 initial conditions. Extensive testing on multiple data sets was used to assess the efficacy of the proposed model, demonstrating its ability to accurately predict the system’s evolution as well as to generalize. We provide a thorough comparison study, comparing our suggested design to a few other comparable methods using experiments on various PDEs, such as the Kuramoto-Sivashinsky equation and Burger’s equation. Results show improved accuracy, highlighting the capabilities of the transformer-based Koopman autoencoder. The proposed architecture is significantly ahead of other architectures, in terms of solving different types of PDEs using a single architecture. Our method relies entirely on the data, without requiring any knowledge of the underlying equations. This makes it applicable to even the data sets where the governing equations are not known.

PMID:40310706 | DOI:10.1063/5.0244221

J Otolaryngol Head Neck Surg. 2025 Jan-Dec;54:19160216251333359. doi: 10.1177/19160216251333359. Epub 2025 May 1.

ABSTRACT

ImportanceUnlike other head and neck cancers, head and neck soft tissue sarcoma (HN-STS) is staged similarly to sarcomas in the trunk and extremities. The current American Joint Committee on Cancer (AJCC) staging system has limitations that hinder accurate prognosis prediction for HN-STS.ObjectiveWe aimed to develop a novel location-grading-node-metastasis (LGNM) staging system based on the primary tumor location to more accurately stratify prognosis for HN-STS.DesignA retrospective case series from 1990 to 2021.Setting/ParticipantsThis study included 471 patients diagnosed with HN-STS at Sun Yat-sen University Cancer Center between 1990 and 2021.Main outcome measuresIn the primary analysis, we obtained the overall survival (OS) rate. Secondary measures included area under the receiver operating characteristic curve, Harrell’s C, Somers’ D, Gönen and Heller’s K, O’Quigley’s ρ2k, Royston’s R², the Bayesian information criterion for concordance, and variation in patient outcomes.ResultsThe eighth edition of AJCC T classification for tumor size inadequately conveys prognosis information. In contrast, the primary tumor location and local invasion are prognostic factors for HN-STS and categorized into 4 stages: L1 (low risk: scalp, face, supraclavicular, ear), L2 (intermediate risk: neck, paravertebral, pharynx, tonsil, eye, orbit), L3 (high risk: cavity, lip, palate, buccal mucosa, salivary gland, maxilla, mandible), and L4 (any location with local invasion). The new LGNM staging system effectively distributed patients into stages I to IV, with statistically-significant survival differences among these stages. Five-year OS rates were 96.9% for stage I, 78.4% for stage II, 37.1% for stage III, and 7.1% for stage IV (P < .001). Additionally, the LGNM staging system demonstrated superior predictive ability and concordance compared with the seventh and eighth editions of AJCC staging systems.Conclusions/RelevanceThe LGNM staging system shows better homogeneity and discriminatory power than the AJCC system, improving risk stratification and prognosis prediction in HN-STS.

PMID:40310697 | DOI:10.1177/19160216251333359