Working toward benchmarks in orthopedic OR efficiency for joint replacement surgery in an academic centre
Bibliographic record
Abstract
BACKGROUND: The introduction of 4-joint operating rooms (ORs) to meet provincial wait time targets represented a major change in practice, providing an opportunity to optimize patient care within an OR time allotment of 8 hours. We reviewed our success rate completing 4 joint replacements within 8 hours and defined benchmarks for successful completion. METHODS: We reviewed the surgeries performed in the 4-joint ORs between May and October 2012. Using prospectively collected data from the Surgical Information Management System, each surgery time was divided into the following components: anesthesia preparation time (APT), surgical preparation time (SPT), procedure duration, anesthesia finishing time (AFT) and turnover time. We defined success as 4 joint replacements being completed within the allotted time. RESULTS: We reviewed 49 4-joint OR days for a total of 196 joint surgeries. Of the 49 days, 24 (49%) were successful. Only 2 surgeons had a success rate greater than 50%. Significant predictors of success were APT (odds ratio 1.09, 95% confidence interval [CI] 1.02-1.16), procedure duration (odds ratio 1.02, 95% CI 1.00-1.05) and AFT (odds ratio 1.19, 95% CI 1.06-1.34). We calculated probabilities for each component and derived benchmark times corresponding to the probability of 0.60. These benchmarks were APT of 9 min, SPT of 14 min, procedure duration of 68 min, AFT of 4 min and turnover of 15 min. CONCLUSION: We established benchmark times for the successful completion of 4 primary joint replacements within an 8-hour shift. Targeted interventions could maximize OR efficiency and enhance multidisciplinary care delivery.
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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 | 0.038 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".