Redesigning operating room booking in a tertiary care academic centre during the COVID-19 pandemic
Bibliographic record
Abstract
SUMMARY: With the closure of most operating rooms (ORs) during the coronavirus disease 2019 (COVID-19) pandemic, the traditional allocation of block OR time needed to be redesigned. An important factor permitting the treatment of patients in a prioritized fashion was our pre-existing centralized OR booking (CORB) framework, which already required surgeons to categorize the priority level for each patient. The CORB, in conjunction with the multidisciplinary OR oversight committee that was formed during COVID-19 to review and triage the urgent cases, allowed for prioritization of cases among surgical services. Centralized OR booking provided opportunities that were essential in OR planning during the pandemic, including the ability to plan surgeries to maximize OR efficiency, minimize the number of admissions on any given day to the wards and the intensive care unit, flatten the number of admissions over the week and provide the flexibility to ramp up or down the number of ORs as the crisis changed.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".