Delays in the operating room: signs of an imperfect system.
Bibliographic record
Abstract
BACKGROUND: Delays in the operating room have a negative effect on its efficiency and the working environment. In this prospective study, we analyzed data on perioperative system delays. METHODS: One neurosurgeon prospectively recorded all errors, including perioperative delays, for consecutive patients undergoing elective procedures from May 2000 to February 2009. We analyzed the prevalence, causes and impact of perioperative system delays that occurred in one neurosurgeon's practice. RESULTS: A total of 1531 elective surgical cases were performed during the study period. Delays were the most common type of error (33.6%), and more than half (51.4%) of all cases had at least 1 delay. The most common cause of delay was equipment failure. The first cases of the day and cranial cases had more delays than subsequent cases and spinal cases, respectively. A delay in starting the first case was associated with subsequent delays. CONCLUSION: Delays frequently occur in the operating room and have a major effect on patient flow and resource utilization. Thorough documentation of perioperative delays provides a basis for the development of solutions for improving operating room efficiency and illustrates the principles underlying the causes of operating room delays across surgical disciplines.
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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 | 0.002 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".