Bibliographic record
Abstract
With the growing rate of surgical expenditures, improving operating room efficiency has become one of the most important targets for health care providers. Any delays, cancellations, or no-shows, result in increased costs for a hospital. In addition, it is difficult to predict the length of surgery procedures due to the variability inherent in surgery procedure times and account for emergency cases. Effective appointment schedules, which minimize the costs of patient waiting time and surgeon idle time and overtime, play an important role in terms of improving efficiency in hospital operating rooms. This research is to develop scheduling policies for elective and emergency surgeries with the objective of reducing waiting time, idle time and overtime. Simulation-based modeling is used to formulate and evaluate different scheduling policies under different operating conditions including different distributions for surgery duration, multiple types of surgical procedures, the arrival of emergency cases and different levels of cost coefficients for idle time and overtime. These factors have not been simultaneously studied in prior studies. The modeling framework is able to account for the significant uncertainty and complexity present in this problem setting. Historical surgery procedure data over a two-year period from the Canadian Institute for Health Information (CIHI) database is used to provide empirical support for the input parameters of the model and validate the efficiency of the different scheduling policies.
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.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".