Spreadsheet Model Helps to Assign Medical Residents at the University of Vermont's College of Medicine
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
This paper describes a spreadsheet model that MBA students enrolled in an MS course constructed to replace the manual method of assigning medical residents in radiology to on-call and emergency rotations at the University of Vermont's College of Medicine. Although it contains more than 10,000 variables, the model was easy to build and solve by practitioners who are “lightly educated” in OR/MS. Based on this group's work, we discuss an approach that end-user practitioners can take to create spreadsheet optimization models. We also provide several observations and argue that spreadsheet models can provide an alternative scheduling method for problems of a smaller scope. Despite the major advances in personnel-scheduling methodologies and software, manual scheduling is still the predominant method used for such smaller-scope problems.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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