Improving on-time surgical starts in an operating room.
Bibliographic record
Abstract
BACKGROUND: Operating rooms are expensive to run, and hospitals strive to be efficient. The purpose of this study was to evaluate an initiative to improve starting on time in the operating room in an academic pediatric hospital. METHODS: We used an 8-step approach to transforming an organization. A multidisciplinary team defined on-time starts, identified reasons for delays and instituted changes, including improving the same-day admission process, instituting a huddle of operating room staff each morning and providing feedback about on-time starts to staff. RESULTS: The most common reasons for delay were surgeon and anesthesiologist unavailability and lack of preparedness of patients. The percentage of operations that began on time, defined as the patient being in the room, increased from about 6% to 60% over a 9-month period. CONCLUSION: A targeted, multifaceted and multidisciplinary approach can increase the percentage of operations that begin on time in a pediatric hospital.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".