Operating Room Utilization: A Retrospective Analysis of Perioperative Delays
Bibliographic record
Abstract
Objective: Avoiding surgical delay in the operating room (OR) is essential to provide timely, safe, and cost-effective care. The objective of this study was to identify the causes of OR delays and assess trends related to surgical specialties or approaches. Materials and Methods: This retrospective study included all elective gynecology (Gyn), general surgery (GS), and urology (Uro) surgeries performed during a 12-month period. Operative case details, surgical times, and reasons for delays were retrieved from a perioperative database. Representative specialty-specific procedures (hysterectomy, colectomy, nephrectomy) were chosen and compared separately to assess trends in delays. Results: A total of 4206 surgeries were completed during the study period; of which 1447 (34%) were Gyn, 2226 (52%) GS, and 533 (12%) Uro. Delays occurred in 1010 Gyn (70%), 1304 GS (59%), and 225 Uro cases (48%). The most-common reason for delay was case-related—delays in case due to delays in prior cases—(n = 1171 [28%]), followed by patient-related delays (n = 458 [11%]), then delays in patient preparedness (n = 340 [8%]). These rates of delay frequency were similar across representative specialty-specific procedures. Delays were more frequent when comparing laparoscopic to open colectomy (38/51, 75% versus 24/25, 96%; p = 0.02), whereas there was no significant difference in delays between laparoscopic and open hysterectomy (162/198, 82% versus 50/68, 74%; p = 0.07) or nephrectomy (4/7, 57% versus 35/76, 46%; p = 0.70). Conclusions: This study suggests that among specialties and cases, the most-common cause of delays was case-related. These findings provide a platform from which to introduce quality-improvement initiatives. (J GYNECOL SURG 36:109)
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".