A Stochastic Surgery Sequencing Model Considering the Moral and Human Virtues
Bibliographic record
Abstract
In this research, we will work sequencing problem of patients demanding surgery under uncertainty in times (including the surgical time, time to prepare the operating room, patient awake time before transferring to the recovery room …). For this problem, a stochastic mixed integer programming model as Stochastic Surgery Sequencing Model (S3M) has been developed.Since this model is achance-constraint problem, which makes it very complex. This problem aims to minimize the cost of operating room personnel overtime and to reduce patient’s waiting time. In mathematical schedule models, we consider three level of patient’s priority ( ). Based on these moral and human dimensions, decision maker can prioritize patients. Restrictions on the balance the operating roomsand priorities for patients are all from the real-world constraints, are included in this issue. A branch-reduce-cut algorithm is used to solve the model.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 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".