Predicting Duration of Surgery to Schedule Elective Orthopaedic Surgery
Bibliographic record
Abstract
Objectives:The aim of this thesis was to determine if a two-stage approach utilizing a machine learning (ML) model for prediction of duration of surgery (DOS) paired with schedule optimization improves operating room over/underutilization compared to using the mean duration. Materials and Methods: ML models were compared to determine the best model for predicting DOS and length of stay (LOS) using patient factors. DOS predictions were used as an input to three schedule optimization formulations, which were evaluated using the true labels. Results: Neural networks performed best in predicting DOS and LOS based on mean squared error. The Split and MSSP formulations yielded similar results and performed significantly better than the Any formulation. The schedules that utilized ML-predicted DOS outperformed the mean surgery duration over all schedule hyperparameters, with an average overtime reduction of 300-500 minutes/week. Conclusion: Optimizing surgical scheduling using a predict-then-optimize approach improves operating room efficiency.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".