High-throughput Operating Room System for Joint Arthroplasties Durably Outperforms Routine Processes
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: Recent publications have focused on increased operating room (OR) throughput without increasing total OR time. The authors hypothesized that a system of parallel processing for lower extremity joint arthroplasties sustainably reduces nonoperative time and increases throughput. METHODS: The high-throughput parallel processing strategy included neuraxial anesthesia performed in an "induction room" adjacent to the OR, patient selection, an additional circulating nurse, and end-of-case transfer of care to a recovery room nurse who transported the patient from the OR to recovery. Instruments and supplies were prepared in a dedicated sterile setup area. Data were extracted from administrative databases. Group comparisons used standard statistical methods; statistical process control was used to evaluate performance over time. RESULTS: There were 688 historic control cases from 299 days over 16 months, and 905 high-throughput cases from 304 days spanning 24 consecutive months starting September 1, 2004. Throughput increased from 2.6 +/- 0.7 (mean +/- SD) to 3.4 +/- 0.8 arthroplasties per day per room. Nonoperative time decreased by 36 min (or 50%) per case. Operative time also decreased by 14 min (12%) per case. The end time for the high-throughput OR day was only 16 min later than control. Nonoperative time, operative time, and throughput remained significantly improved after 2 yr of operation. Contribution margin increased 19.6%. CONCLUSION: Reorganizing the perioperative work process for total joint replacements sustainably increased OR throughput. Because joint arthroplasties generated a positive margin greater than the incremental cost, the high-throughput system improved financial performance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 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) | 0.000 | 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