Tag: pubmed

Eur J Pediatr. 2025 Oct 11;184(11):677. doi: 10.1007/s00431-025-06511-z.

ABSTRACT

The respiratory management of neonates with hypoxic-ischemic encephalopathy (HIE) undergoing therapeutic hypothermia (TH) is a challenging issue, but remains poorly standardized. The aim of this study was to describe current practice regarding respiratory management, particularly tracheal intubation, in neonates undergoing TH in France and French-speaking Belgium. In this binational cross-sectional study, current practices about respiratory management were collected by an online-based survey sent to all tertiary neonatal intensive care units providing TH in France and French-speaking Belgium. Practices were analyzed to assess their heterogeneity and compared between centers that routinely intubate for TH and those that did not. The survey was completed by 57/68 (84%) centers. Routine intubation during TH was reported by 33 (58%) centers. Motivations behind routine intubation during TH included concerns about pain, discomfort, and neuroprotection. Among the 24 other centers, there was significant heterogeneity in the respiratory care of patients, while practices regarding sedation-analgesia appeared more consensual. The country was the only characteristic independently associated with routine intubation during TH (France vs Belgium: OR = 9.333, 95% CI 1.035-84.202, p = 0.047).

CONCLUSION: Respiratory management of neonates with HIE undergoing TH is highly heterogeneous. Most centers routinely intubate neonates undergoing TH, especially in France. These findings highlight the need for more evidence-based studies and guidelines to optimize respiratory care during TH.

WHAT IS KNOWN: • For neonates with hypoxic-ischemic encephalopathy, evidence to guide respiratory management during therapeutic hypothermia remains scarce. • Yet, ventilation is a crucial aspect of the neuroprotective management of these neonates.

WHAT IS NEW: • This binational survey reveals that most centers routinely intubate neonates undergoing therapeutic hypothermia. • Our findings also suggest significant heterogeneity and may help to design future studies.

PMID:41076488 | DOI:10.1007/s00431-025-06511-z

Environ Monit Assess. 2025 Oct 11;197(11):1198. doi: 10.1007/s10661-025-14644-9.

ABSTRACT

Understanding spatial and temporal patterns of river water quality over a multi-year period is crucial for effective basin management and pollution control. This study applies functional data analysis (FDA) to evaluate monthly water quality index (WQI) data from 16 monitoring stations across the Klang River Basin, Malaysia, covering the period from 2020 to 2023, which spans both pre- and post-pandemic conditions. By treating water quality index (WQI) measurements as smooth functions over time, FDA captures underlying trends and variations that are not readily detected using classical statistical techniques. Functional principal component analysis (FPCA) reveals that the first component accounts for 97% of the total variation, reflecting the dominant pattern in water quality over time, which is characterized by relatively stable upstream conditions and gradual deterioration downstream. The second and third components capture seasonal fluctuations and short-term disturbances, potentially linked to monsoonal cycles and shifts in human activities during the pandemic. Functional clustering based on FPCA scores groups stations according to their temporal behavior, distinguishing upstream areas with stable conditions from downstream areas experiencing greater variability. Spatial interpretation of these clusters offers additional insight into localized pollution sources and environmental stressors. Compared to classical PCA, FDA provides a more detailed, curve-based understanding of time-dependent and location-specific changes in water quality. The result underscore the value of FDA in environmental monitoring, particularly for detecting pre- and post-pandemic shifts, and support its application in guiding adaptive and spatially targeted management strategies for river basins.

PMID:41076480 | DOI:10.1007/s10661-025-14644-9

Eur Radiol. 2025 Oct 11. doi: 10.1007/s00330-025-12050-w. Online ahead of print.

ABSTRACT

OBJECTIVES: Accurate preoperative classification of pulmonary nodules (PNs) is critical for guiding clinical decision-making and preventing overtreatment. This study aims to evaluate the predictive performance of artificial intelligence (AI)-based quantitative computed tomography (CT) feature analysis in differentiating among four pathological types of PNs: atypical adenomatous hyperplasia and adenocarcinoma in situ (AAH + AIS), minimally invasive adenocarcinoma (MIA), invasive adenocarcinoma (IAC), and lung inflammatory nodules (IN).

MATERIALS AND METHODS: A total of 462 pathologically confirmed PNs were analyzed. Radiomic features, including CT attenuation metrics, 3D morphometrics, and texture parameters such as entropy and skewness, were extracted using a deep learning-based AI platform. Logistic regression models were constructed using both single- and multi-variable strategies to evaluate the classification accuracy of these features. Moreover, the inclusion of IN as a separate category significantly enhanced the clinical utility of AI in differentiating benign mimickers from malignant nodules. The combined model, which integrated AI-derived features with traditional CT signs, was used to assess the diagnostic performance of the radiomic features in differentiating the four pathological types of nodules.

RESULTS: The combined model demonstrated superior diagnostic performance, with area under the curve (AUC) values of 0.936 for IAC, 0.884 for AAH + AIS, and 0.865 for IN. Although MIA showed lower classification accuracy (AUC = 0.707), key features such as entropy, solid component ratio, and total volume effectively distinguished invasive from non-invasive lesions.

CONCLUSION: These findings highlight the potential of AI-enhanced radiomics for supporting non-invasive, objective, and individualized diagnosis of PNs.

KEY POINTS: Question Can artificial intelligence (AI)-based quantitative CT analysis reliably differentiate benign inflammatory nodules from the spectrum of lung adenocarcinoma subtypes, a common diagnostic challenge? Findings An integrated model combining AI-driven radiomic features and traditional CT signs demonstrated high accuracy in differentiating invasive adenocarcinoma (AUC = 0.936), pre-invasive lesions (AUC = 0.884), and inflammatory nodules (AUC = 0.865). Clinical relevance AI-enhanced radiomics provides a non-invasive, objective tool to improve preoperative risk stratification of pulmonary nodules, potentially guiding personalized management and reducing unnecessary surgeries for benign inflammatory lesions that mimic malignancy.

PMID:41076471 | DOI:10.1007/s00330-025-12050-w

Eur Radiol. 2025 Oct 11. doi: 10.1007/s00330-025-12048-4. Online ahead of print.

ABSTRACT

OBJECTIVES: Fractional flow reserve (FFR) and instantaneous wave-Free Ratio (iFR) pressure measurements during invasive coronary angiography (ICA) are the gold standard for assessing vessel-specific ischemia. Artificial intelligence has emerged to compute FFR based on coronary computed tomography angiography (CCTA) images (CT-FFR_AI). We assessed a CT-FFR_AI deep learning model for the prediction of vessel-specific ischemia compared to invasive FFR/iFR measurements.

MATERIALS AND METHODS: We retrospectively selected 322 vessels from 275 patients at two centers who underwent CCTA and invasive FFR and/or iFR measurements during ICA within three months. A junior and senior radiologist at each center supervised vessel centerline-building to generate curvilinear reformats that were processed for CT-FFR_AI binary outcomes (≤ 0.80 or > 0.80) prediction. Reliability for CT-FFR_AI outcomes based on radiologists’ supervision was assessed with Cohen’s κ. Diagnostic values of CT-FFR_AI were calculated using invasive FFR ≤ 0.80 (n = 224) and invasive iFR ≤ 0.89 (n = 238) as the gold standard. A multinomial logistic regression model, including all false-positive and false-negative cases, assessed the impact of patient- and CCTA-related factors on diagnostic values of CT-FFR_AI.

RESULTS: Concordance for CT-FFR_AI binary outcomes was substantial (κ = 0.725, p < 0.001). Sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of CT-FFR_AI in predicting vessel-specific ischemia on a per-vessel analysis, based on senior radiologists’ evaluations, were 85% (58/68) and 91% (78/86), 82% (128/156) and 78% (119/152), 67% (58/86) and 70% (78/111), 93% (128/138) and 94% (119/127), and 83% (186/224) and 83% (197/238), respectively. Coronary calcifications significantly reduced the diagnostic accuracy of CT-FFR_AI (p < 0.001; OR, 1.002; 95% CI 1.001-1.003).

CONCLUSION: CT-FFR_AI demonstrates high diagnostic performance in predicting vessel-specific coronary ischemia compared to invasive FFR and iFR. Coronary calcifications negatively affect specificity, suggesting that further improvements in spatial resolution could enhance accuracy.

KEY POINTS: Question How accurately can a new deep learning model (CT-FFR_AI) assess vessel-specific ischemia from CCTA non-invasively compared to two validated pressure measurements during invasive coronary angiography? Findings CT-FFR_AI achieved high diagnostic accuracy in predicting vessel-specific ischemia, with high sensitivity and negative predictive value, independent of scanner type and radiologists’ experience. Clinical relevance CT-FFR_AI provides a non-invasive alternative to Fractional Flow Reserve and instantaneous wave-Free Ratio measurements during invasive coronary angiography for detecting vessel-specific ischemia, potentially reducing the need for invasive procedures, lowering healthcare costs, and improving patient safety.

PMID:41076470 | DOI:10.1007/s00330-025-12048-4

J Robot Surg. 2025 Oct 11;19(1):681. doi: 10.1007/s11701-025-02884-3.

ABSTRACT

The study design is retrospective study. Robotic spine surgery has evolved rapidly over the past two decades, with growing adoption. However, research on error sources in robotic-assisted pedicle screw placement remains scarce. This study investigates the incidence, risk factors, and intraoperative error types linked to screw malposition. We retrospectively analyzed patients who underwent robotic-assisted thoracic and lumbar spine surgeries at our center since Oct 2023. The incidence of screw breaches, intraoperative errors, and risk factors, such as BMI, pathology, and surgical approach, were assessed. Screw malpositions were identified on postoperative O-arm scans and classified by error cause. Intraoperative revision protocols were documented. A total of 1060 patients (5644 pedicle screws) were included. Screw malposition occurred in 13 patients (1.2%) involving 22 screws (0.39%). The mean BMI of patients with malposition was 27.3 versus 26.5 overall. Among the 13 cases, 8 had lumbar degenerative pathology, 3 scoliosis, 1 high-grade listhesis, and 1 revision for proximal junctional failure. Skiving was the most frequent error (6 cases), followed by arm shift (4), patient movement (2), and registration error (1). Learning curve had a significant role with 12/22 screw malpositioning happening in the initial 50 robotic cases. No significant correlation was found between malposition and pathology type or surgical approach. Robotic systems improve pedicle screw accuracy but are not immune to errors. Skiving is the most common issue and can be minimized by selecting flat bony entry points and avoiding sloping surfaces. Other causes include arm shift, patient movement, and registration errors.

PMID:41076469 | DOI:10.1007/s11701-025-02884-3

Clin Rheumatol. 2025 Oct 11. doi: 10.1007/s10067-025-07722-3. Online ahead of print.

ABSTRACT

AIM: This study aimed to compare talar cartilage thickness in patients with RA and healthy controls and to investigate its association with clinical and demographic variables.

METHOD: Thirty-seven healthy controls and 63 patients with RA diagnosed using the American College of Rheumatology’s (ACR) 2010 criteria were included in this cross-sectional observational study. All participants’ age, gender, and body mass index (BMI) were recorded. The hospital record system’s data was retrieved for the patient group, including medication use, C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), seropositivity, and disease duration. VAS was used to assess pain, and DAS-28 was used to assess disease activity. Talar cartilage thickness measurement was performed by the same doctor as the ultrasound. Descriptive statistics of the data obtained from the study were given by mean, standard deviation for numerical variables, and frequency and percentage analysis for categorical variables. Mann-Whitney U test was used for categorical variables with two groups, and Kruskal Wallis test was used for categorical variables with three or more groups in the comparison of parameters according to categorical variables. Analyses were performed with the help of SPSS 22.0 program. p < 0.05 significance level was selected.

RESULTS: There was no discernible difference between the patient and control groups in terms of age, height, weight, gender, or BMI values (p > 0.05). Talar cartilage thickness did not significantly correlate with age, BMI, ESR, CRP, CCP, DAS-28 RF, number of swollen joints, or sensitive joints (p > 0,05). However, talar cartilage thickness, VAS, HAQ values, and disease duration were significantly correlated negatively (p < 0.05).

CONCLUSION: Our study showed that there is a significant decrease in talar cartilage thickness in RA patients, and this decrease is associated with disease duration, pain severity, and functional impairment. Key Points • Talar cartilage thickness was found to be lower in RA patients compared to the control group • Talar cartilage measurements were negatively correlated with disease duration, VAS and HAQ scores • Talar cartilage thickness may be a potential biomarker for assessing early joint damage and monitoring disease progression in RA.

PMID:41076467 | DOI:10.1007/s10067-025-07722-3

J Robot Surg. 2025 Oct 11;19(1):680. doi: 10.1007/s11701-025-02828-x.

ABSTRACT

Robotic-assisted total knee arthroplasty (RA-TKA) aims to improve outcomes and reduce costs, but its effect on in-hospital direct costs remains unclear. This study compares in-hospital direct costs between RA-TKA and conventional TKA (C-TKA). A retrospective review of elective TKAs from 1/2018-5/2023 at a single center was performed. RA-TKA and C-TKA patients were matched by demographics and surgery date. Cost metrics included total, fixed, variable direct costs and indirect costs. Operative times, length of stay (LOS), and in-hospital costs were compared using independent t-tests, Mann-Whitney U, and Chi-square tests. Pearson correlations assessed relationships between costs and clinical metrics. A hierarchical multilinear model was used to control for demographics, operative time, and LOS to isolate the cost of robotic assistance. Of 2590 primary TKAs, 440 used robotic assistance. After matching, 788 patients remained (397 RA-TKA, 391 C-TKA). Preoperative Knee Injury and Osteoarthritis Outcome Score for Joint Replacement (KOOS JR) and Veterans Rand 12 Item Health Survey (VR-12) scores were similar. RA-TKAs had longer operative times (“cut-to-close”: 128 vs. 110 min; “room-in to room-out”: 202 vs. 182 min; p < 0.001) and longer LOS (2.39 vs. 1.56 days; p < 0.001). RA-TKAs had 17.8% higher total direct costs and 18.5% higher variable direct costs (p < 0.001). After adjusting for operative time and LOS, robotic assistance independently increased total direct cost by 4.2% of median C-TKA cost (p < 0.001). Robotics remained an independent predictor of total and variable direct cost (p < 0.001). RA-TKA incurs higher in-hospital direct costs than C-TKA, largely due to increased variable costs. Even after adjusting for operative time and LOS, robotic assistance independently contributes to higher total direct cost.

PMID:41076447 | DOI:10.1007/s11701-025-02828-x

J Prosthet Dent. 2025 Oct 10:S0022-3913(25)00727-9. doi: 10.1016/j.prosdent.2025.09.005. Online ahead of print.

ABSTRACT

STATEMENT OF PROBLEM: Immediate loading of dental implants in the esthetic zone combines implant placement and the interim restoration in a single stage, addressing the demand for reduced treatment duration and enhanced outcomes. The impact of prosthetic workflows, traditional versus digital, on marginal bone loss, esthetic success, and patient satisfaction remains unclear.

PURPOSE: The purpose of this systematic review and meta-analysis was to compare the clinical and patient-centered outcomes of traditional versus digital prosthetic workflows in the immediate loading of implants in the esthetic zone, focusing on marginal bone loss, esthetic outcomes, and patient satisfaction.

MATERIAL AND METHODS: Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines, randomized controlled trials and non-randomized comparative studies published between January 2015 and January 2025 were identified through searches in the PubMed, Cochrane Library, and Scopus databases. Studies reporting on marginal bone loss, Pink and White Esthetic Scores, and patient satisfaction for immediate implant loading were included. Two reviewers independently extracted data and assessed the risk of bias. A qualitative and quantitative synthesis of findings was conducted. Review Manager (RevMan) was used to conduct the meta-analysis. The results were presented with a 95% confidence interval (CI), and the mean difference (MD) was calculated as the summary statistic for continuous outcomes.

RESULTS: Four studies, comprising 2 randomized controlled trials and 2 non-randomized prospective studies, satisfied the inclusion criteria. The meta-analysis showed a non-significant trend favoring digital workflows for patient satisfaction (SMD: 0.26, 95% CI: -0.01 to 0.53; P=.06, I²=0%). Digital workflows demonstrated significant improvements in esthetic outcomes, with higher Pink Esthetic Scores (MD: 0.14, 95% CI: 0.05 to 0.24; P=.003, I²=17%) and White Esthetic Scores (MD: 0.09, 95% CI: 0.01 to 0.17; P=.02, I²=0%). Marginal bone loss was significantly reduced with digital workflows (MD:-0.08, 95% CI: -0.15 to -0.01; P=.02, I²=0%), indicating superior precision compared to conventional workflows.

CONCLUSIONS: Digital workflows for implant placement showed significant advantages in esthetic outcomes and less marginal bone loss, with a trend toward higher patient satisfaction. These findings support the growing adoption of digital workflows in clinical dental practice.

PMID:41076437 | DOI:10.1016/j.prosdent.2025.09.005

J Prosthet Dent. 2025 Oct 10:S0022-3913(25)00764-4. doi: 10.1016/j.prosdent.2025.09.034. Online ahead of print.

ABSTRACT

STATEMENT OF PROBLEM: Existing evidence suggests that conventional and bulk-fill resin composites (RCs) do not fully restore the physiological fracture resistance of teeth with extensive MOD cavities, leaving posterior restorations susceptible to bulk fracture, particularly in structurally compromised or endodontically treated teeth. Whether short fiber-reinforced resin-based composites (SFRCs) can address these limitations and improve fracture resistance remains unclear.

PURPOSE: The purpose of this systematic review and network meta-analysis was to evaluate the fracture resistance of extensive direct restorations restored with SFRCs, either as a single component or combined with other materials, compared with bulk-fill RCs, conventional RC, and intact teeth.

MATERIAL AND METHODS: A systematic search was conducted in the PubMed, Scopus, Web of Science, and EMBASE databases for studies published through July 2025. In vitro studies evaluating fracture resistance in MOD cavities restored with SFRC compared with other restorative techniques were included. A random-effects network meta-analysis was performed using standardized mean differences (SMDs) with 95% confidence intervals (CIs). Heterogeneity (I² and τ²) and inconsistency were assessed using node-splitting and loop inconsistency models. The confidence in the results was evaluated using the confidence in network meta-analysis (CINeMA) framework.

RESULTS: Thirty studies met the inclusion criteria. SFRC with overlay materials demonstrated significantly higher fracture resistance than conventional RC (SMD=1.30; 95% CI: 0.50 to 2.09) but not significantly higher than bulk-fill RC (SMD=0.81; 95% CI:-0.26 to 1.88). SFRC alone also showed greater resistance than conventional RC (SMD=2.12; 95% CI:0.22 to 4.03). Notably, no statistically significant difference was found between SFRC alone and intact teeth (SMD=-0.74; 95% CI:-2.64 to 1.16). Confidence in comparisons ranged from low to moderate, primarily associated with concerns regarding imprecision and heterogeneity. Intact teeth exhibited the highest fracture resistance across all conditions (SUCRA: 95.5%).

CONCLUSIONS: SFRCs exhibited better fracture resistance compared with conventional RCs, while no significant difference was observed between SFRC-based restorations and bulk-fill RCs. SFRCs may serve as a clinically suitable alternative for extensive restorations. Future research should focus on optimizing material combinations and addressing methodological variability in in vitro studies.

PMID:41076436 | DOI:10.1016/j.prosdent.2025.09.034

Br J Oral Maxillofac Surg. 2025 Sep 23:S0266-4356(25)00243-8. doi: 10.1016/j.bjoms.2025.08.015. Online ahead of print.

ABSTRACT

This study evaluates the performance of six large language models (LLMs) in generating content relevant to oral and maxillofacial surgery (OMFS), focusing on their ability to provide accurate, comprehensive, and relevant information across five specific tasks. Each LLM was assessed based on its responses to five prompts: (1) postoperative instructions for third molar surgery; (2) a list of best-selling books on orthognathic surgery; (3) the most cited articles in OMFS; (4) novel ideas for systematic reviews; and (5) emerging trends in OMFS. Responses were scored for relevance, comprehensiveness, and accuracy using predefined criteria. Statistical analysis was performed using the Kruskal-Wallis test to compare tool performance. The LLMs performed similarly overall, with varying strengths and weaknesses. For postoperative instructions, they all provided comparable recommendations, though Perplexity underperformed. In identifying best-selling books, Gemini and Perplexity excelled, while ChatGPT and Copilot struggled with retrieving highly cited articles. Copilot and Claude were more effective in suggesting novel systematic review topics, while ChatGPT, Claude, Copilot, and DeepSeek identified emerging trends most accurately. LLMs demonstrate significant potential in supporting OMFS-related tasks, but their performance varies depending on the specific application. While they excel at synthesising existing information and identifying trends, limitations in accuracy and occasional hallucinations highlight the need for human oversight. These findings underscore the importance of integrating artificial intelligence (AI) as a supplementary tool in clinical, academic, and research settings, ensuring its use complements, rather than replaces, human expertise.

PMID:41076417 | DOI:10.1016/j.bjoms.2025.08.015