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 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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 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".