Objective: Interlaminar endoscopic lumbar decompression is an increasingly used minimally invasive technique for treating lumbar disc herniation and lumbar spinal canal stenosis. However, technical difficulty varies substantially across cases because of anatomical and patient-related factors. This prospective study evaluated clinical and radiological predictors of surgical ease using an objective Endoscopic Ease Index (EEI). Methods: This prospective observational study included 122 patients who underwent interlaminar endoscopic lumbar decompression between January 2021 and May 2025. Preoperative clinical data and magnetic resonance imaging morphometric parameters—including interlaminar window dimensions, ligamentum flavum (LF) thickness, facet hypertrophy, and canal stenosis—were analyzed. Intraoperative difficulty was quantified using the EEI, which incorporates operative time, docking time, bleeding score, field clarity, and surgeon-rated difficulty. Predictors of difficult surgery (defined as the upper EEI tertile) were examined using multivariable logistic regression. Results: Of 122 cases, 41 (33.6%) were classified as difficult. Difficult cases had significantly narrower interlaminar windows (10.8±1.9 mm vs. 13.6±2.4 mm, p<0.001), greater LF thickness (5.0±0.9 mm vs. 3.6±0.7 mm, p<0.001), and a higher prevalence of facet hypertrophy (58.5% vs. 27.1%, p=0.004). In the multivariable model, 3 variables independently predicted difficult surgery: LF thickness >4 mm (odds ratio [OR], 5.22; p<0.001), interlaminar height <11 mm (OR, 4.48; p=0.001), and facet hypertrophy ≥ grade 2 (OR, 3.27; p=0.02). The predictive model demonstrated excellent discrimination (area under the receiver operating characteristic curve, 0.87). Despite greater operative complexity, postoperative improvements in pain (visual analogue scale) and disability (Oswestry Disability Index) were comparable between groups. Conclusion LF hypertrophy, reduced interlaminar height, and facet hypertrophy independently predict greater technical difficulty during interlaminar endoscopic lumbar decompression. Preoperative identification of these morphometric parameters may improve surgical planning and help optimize outcomes.

Purpose: There is a substantial gap in the scientific literature about specific, evidence-based guidelines for optimal word count (length) in multiple-choice question (MCQ) “stems” and “options” and validated time management strategies for MCQ test-takers. This dearth of research indicates a potential need for more studies to develop and validate these significant aspects of MCQ assessment and strategy. This was the first study which formulated guidelines for the word count of scenario-based MCQ (SB-MCQ) “stems” and “options,” along with justifications, from the perspective of medical education experts. The present research also developed specific, validated time management guidelines to assist test-takers effectively navigate MCQ examination. Methods: A mixed-methods design was implemented where quantitative data from a validated structured questionnaire and qualitative (focus group discussion) data were collected from 76 medical education professionals across multiple Indian provinces, and then analyzed using SPSS and NVivo software. Results: A recommended best practice for writing SB-MCQs was reported to have question stems between 35–50 words followed by 20–35 words, with three-option formats being preferred over four and five options. Nearly 92.73% of participants reported that, during 1-minute timeframe, maximum time must be spent on “analyzing question stem” followed by on “eliminating options.” The study developed succinct, “eight-step guide” to empower test-takers complete MCQ tests proficiently. Conclusion Scenario based MCQs with stems between 35–50 words and options between 1–10 words can improve item quality. Three-option MCQs can be preferred over four- or five-option formats. An emerged “eight-step structured guide” can assist test takers to tackle multiple-choice tests more efficiently and effectively.

Aging of the jawline commonly results in jowl deformity due to skin laxity and descent of subcutaneous tissues. Traditional correction methods, including rhytidectomy, can effectively address these issues but often involve substantial invasiveness and recovery time. A less invasive, targeted approach for isolated jowl correction is desirable. We present a novel surgical method of direct jowl excision using a triangular full-thickness wedge excision combined with local flap advancement. The excision was meticulously planned along relaxed skin tension lines, avoiding underlying muscular and neural structures, and was followed by layered closure. In our case, a 66-year-old woman who was previously dissatisfied with the outcomes of conventional rhytidectomy underwent direct jowl excision. At the 12-month follow-up, the patient demonstrated marked improvement in jowl prominence and jawline contour, with minimal scar visibility along the mandibular border. No complications or adverse outcomes occurred, and patient satisfaction was notably high. Direct jowl excision offers a focused, minimally invasive alternative for patients with isolated jowl deformities. It provides significant aesthetic improvement with low morbidity, rapid recovery, and acceptable scar formation. This technique should be considered a viable adjunct in the surgical toolkit for facial rejuvenation in carefully selected patients.

PURPOSE: Non-operative management (NOM) has been validated for blunt liver and splenic injuries. Literature on continuous intra-abdominal pressure (IAP) monitoring as a part of NOM remains to be equivocal. The study aimed to find any correlation between clinical parameters and IAP, and their effect on the NOM of patients with blunt liver and splenic injury. METHOD: A prospective cross-sectional study conducted at a level I trauma center from October 2018 to January 2020 including 174 patients who underwent NOM following blunt liver and splenic injuries. Hemodynamically unstable patients or those on ventilators were excluded, as well as patients who suffered significant head, spinal cord, and/or bladder injuries. The study predominantly included males (83.9%) with a mean age of 32.5 years. IAP was monitored continuously and the relation of IAP with various parameters, interventions, and outcomes were measured. Data were summarized as frequency (percentage) or mean ± SD or median (Q₁, Q₃) as indicated. χ² or Fisher's exact test was used for categorical variables, while for continuous variables parametric (independent t-test) or nonparametric tests (Wilcoxon rank sum test) were used as appropriate. Clinical and laboratory correlates of IAP < 12 with p < 0.200 in the univariable logistic regression analysis were included in the multivariable analysis. A p < 0.05 was used to indicate statistical significance. RESULTS: Intra-abdominal hypertension (IAH) was seen in 19.0% of the study population. IAH was strongly associated with a high injury severity score (p < 0.001), and other physiological parameters like respiratory rate (p < 0.001), change in abdominal girth (AG) (p < 0.001), and serum creatinine (p < 0.001). IAH along with the number of solid organs involved, respiratory rate, change in AG, and serum creatinine was associated with the intervention, either operative or non-operative (p = 0.001, p = 0.002, p < 0.001, p < 0.001, p = 0.013, respectively). On multivariable analysis, IAP (p = 0.006) and the mean change of AG (p = 0.004) were significantly associated with the need for intervention. CONCLUSION As a part of NOM, IAP should be monitored as a continuous vital. However, the decision for any intervention, either operative or non-operative cannot be guided by IAP values alone.
Humans ; Male ; Adult ; Female ; Wounds, Nonpenetrating/physiopathology* ; Spleen/injuries* ; Prospective Studies ; Cross-Sectional Studies ; Liver/injuries* ; Middle Aged ; Monitoring, Physiologic/methods* ; Pressure ; Abdominal Injuries/physiopathology* ; Intra-Abdominal Hypertension ; Young Adult

Objective: Patients with severe symptomatic aortic stenosis are assessed for coronary artery disease (CAD) prior to transcatheter aortic valve implantation (TAVI) with treatment implications. Invasive coronary angiography (ICA) is the recommended modality but is associated with peri-procedural complications. Integrating machine learning (ML)-based computed tomography-derived fractional flow reserve (CT-FFR) into existing TAVI-planning CT protocol may aid exclusion of significant CAD and thus avoiding ICA in selected patients. Materials and Methods: A single-center, retrospective study was conducted, 41 TAVI candidates with both TAVI-planning CT and ICA performed were analyzed. CT datasets were evaluated by a ML-based CT-FFR software. Beta-blocker and nitroglycerin were not administered in these patients. The primary outcome was to identify significant CAD. The diagnostic performance of CT-FFR was compared against ICA. Results: On per-patient level, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and diagnostic accuracy were 89%, 94%, 80%, 97% and 93%, respectively. On per-vessel level, the sensitivity, specificity, PPV, NPV and diagnostic accuracy were 75%, 94%, 67%, 96% and 92%, respectively. The area under the receiver operative characteristics curve per individual coronary vessels yielded overall 0.90 (95% confidence interval 85%–95%). ICA may be avoided in up to 80% of patients if CT-FFR results were negative. Conclusion ML-based CT-FFR can provide accurate screening capabilities for significant CAD thus avoiding ICA. Its integration to existing TAVI-planning CT is feasible with the potential of improving the safety and efficiency of pre-TAVI CAD assessment.

Purpose: Implant identification is a pressing concern in dental implantology, and artificial intelligence (AI) has been evaluated for this purpose. YOLO, a state-of-the-art object detection model, is suitable for medical imaging; therefore, this study assessed YOLOv11-the latest iteration-for identifying 10 implant types in Indian clinical settings and compared its accuracy to that of dental professionals. Materials and Methods: A dataset of 3,161 radiographs, comprising both periapical and panoramic images of 10implant types, was annotated and used to train and test YOLOv11. Training was performed on Google Colab using an NVIDIA Tesla T4 GPU (16 GB VRAM). A random sample of 200 radiographs was selected from the test dataset andpresented to 50 dental practitioners for implant identification. Their responses were analysed and compared, using the chi-square test for statistical significance. Results: YOLOv11 achieved precision of 0.87, recall of 0.85, an F1-score of 0.86, and an mAP50 of 0.899. The model achieved excellent classification accuracy for Adin (95%), MIS (94%), Bego (92%), ITI (96%), and Bicon (97%).Moderate accuracy was noted for Noris (82%), Osstem (85%), AlphaBio (88%), Dentium (77%), and Bioline (75%).YOLOv11 demonstrated higher overall accuracy and consistency than dental professionals. Dentists’ accuracy ranged from 27% to 49%, whereas that of YOLOv11 ranged from 92% to 100%. Conclusion YOLOv11 recognised most implant classes with over 90% accuracy, surpassing traditional manualtechniques in implant detection. Although the model is dependable and efficient, certain aspects require improvement.The study also emphasises the significance of a region-specific approach for clinical relevance.