Reducing elective general surgery cancellations at a Canadian hospital
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: In Canadian hospitals, which are typically financed by global annual budgets, overuse of operating rooms is a financial risk that is frequently managed by cancelling elective surgical procedures. It is uncertain how different scheduling rules affect the rate of elective surgery cancellations. METHODS: We used discrete event simulation modelling to represent perioperative processes at a hospital in Toronto, Canada. We tested the effects of the following 3 scenarios on the number of surgical cancellations: scheduling surgeons' operating days based on their patients' average length of stay in hospital, sequencing surgical procedures by average duration and variance, and increasing the number of postsurgical ward beds. RESULTS: The number of elective cancellations was reduced by scheduling surgeons whose patients had shorter average lengths of stay in hospital earlier in the week, sequencing shorter surgeries and those with less variance in duration earlier in the day, and by adding up to 2 additional beds to the postsurgical ward. CONCLUSION: Discrete event simulation modelling can be used to develop strategies for improving efficiency in operating rooms.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.004 | 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