Comparison of Predictive Models for Prevention of Missed Endoscopy Appointments- failure of a Predictive Model to Outperform Overbooking Model
Bibliographic record
Abstract
BACKGROUND: Patient late cancelation and nonattendance for endoscopy appointments is an ongoing problem affecting the productivity and wait times of endoscopy units. Previous research evaluated a model for predictive overbooking and had promising results. STUDY: All endoscopy visits at an outpatient endoscopy unit during 4 nonconsecutive months were included in the data analysis. Patients who did not attend their appointment, or canceled with 48 hours of their appointment were considered nonattendees. Demographic, health, and prior visit behavior data was collected and the groups compared. RESULTS: 1780 patients attended 2331 visits in the study period. Comparing the attendee versus non-attendees, there were significant differences in mean age, prior absenteeism, prior cancelations, and total number of hospital visits. No significant differences were seen between groups in winter versus non-winter months, the day of the week, sex distribution, type of procedure booked, or whether the referral was from specialist clinic or direct to procedure. The visit cancelation proportion (calculated excluding current visit) was substantially higher in the absentee group ( P <0.0001). A predictive model was developed and compared to current booking as well as a straight overbooking of 7%. Both overbooking models performed better than the current practice, but the predictive overbooking model did not outperform straight overbooking. CONCLUSIONS: Developing an endoscopy unit specific predictive model may not be more beneficial than straight overbooking as calculated by missed appointment percentage.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".