Delays in the operating room: signs of an imperfect system.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it