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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".