Development and Evaluation of a Novel Instrument to Measure Severity of Intraoperative Events Using Video Data
Bibliographic record
Abstract
OBJECTIVE: To develop and evaluate a novel instrument to measure SEVERE processes using video data. BACKGROUND: Surgical video data can serve an important role in understanding the relationship between intraoperative events and postoperative outcomes. However, a standard tool to measure severity of intraoperative events is not yet available. METHODS: Items to be included in the instrument were identified through literature and video reviews. A committee of experts guided item reduction, including pilot tests and revisions, and determined weighted scores. Content validity was evaluated using a validated sensibility questionnaire. Inter-rater reliability was assessed by calculating intraclass correlation coefficient. Construct validity was evaluated on a sample of 120 patients who underwent laparoscopic Roux-en-Y gastric bypass procedure, in which comprehensive video data was obtained. RESULTS: SEVERE index measures severity of 5 event types using ordinal scales. Each intraoperative event is given a weighted score out of 10. Inter-rater reliability was excellent [0.87 (95%-confidence interval, 0.77-0.92)]. In a sample of consecutive 120 patients undergoing gastric bypass procedures, a median of 12 events [interquartile range (IQR) 9-18] occurred per patient and bleeding was the most frequent type (median 10, IQR 7-14). The median SEVERE score per case was 11.3 (IQR 8.3-16.9). In risk-adjusted multivariable regression models, history of previous abdominal surgery (P = 0.02) and body mass index (P = 0.005) were associated with SEVERE scores, demonstrating construct validity evidence. CONCLUSION: The SEVERE index may prove to be a useful instrument in identifying patients with high risk of developing postoperative complications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".