A simulation study of scheduling clinic appointments in surgical care: individual surgeon <i>versus</i> pooled lists
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Bibliographic record
Abstract
The purpose of this paper is to compare two methods of scheduling outpatient clinic appointments in the setting where the availability of surgeons for appointments depends on other clinical activities. We used discrete-event simulation to evaluate the likely impact of the scheduling methods on the number of patients waiting for appointments, and the times to appointment and to surgery. The progression of individual patients in a surgical service was modelled as a series of updates in patient records in reaction to events generated by care delivery processes in an asynchronous fashion. We used the Statecharts visual formalism to define states and transitions within each care delivery process, based on detailed functional and behavioural specifications. Our results suggest that pooling referrals, so that clinic appointments are scheduled with the first available surgeon, has a differential impact on different segments of patient flow and across surgical priority groups.
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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 | 0.009 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it