Patient and appointment characteristics associated with no-shows at a multicenter retina ophthalmology practice
Bibliographic record
Abstract
OBJECTIVE: To identify factors associated with no-shows in a multicenter retina subspecialty practice. DESIGN: Cross-sectional study. PARTICIPANTS: Appointments scheduled at a multicenter practice in Fraser Valley, British Columbia, from September 1, 2020, to September 1, 2021. METHODS: A query was conducted on appointments scheduled at a multicenter practice in Fraser Valley, British Columbia, from September 1, 2020, to September 1, 2021. Characteristics, including age, sex, commute distance, appointment date and time, and appointment type, were analyzed using multivariate logistic regression to assess association with no-show status. RESULTS: Of 14,597 appointments, 803 (5.50%) were no-shows. Younger age was associated with higher no-show rates (odds ratio [OR] for <30 years vs 30 to <60 years: 1.46, 95% CI: 1.08-1.98; p < 0.001; vs 60 to <90: 2.13; vs ≥90 years: 1.72). Males were more likely to miss appointments than females (OR: 1.43, CI: 1.24-1.67; p < 0.001). No-shows were more frequent in the fall (OR for Fall vs Winter: 1.29, CI: 1.04-1.60; p = 0.018), scheduled in morning (OR for AM vs PM: 1.31, CI: 1.06-1.62; p = 0.013), 6-month follow-ups (OR: 1.69, CI: 1.15-2.48; p = 0.008), returning patient re-referrals (OR: 1.92, CI: 1.11-3.33; p = 0.020), and new patient referrals (OR: 1.76, CI: 1.05-2.97; p = 0.034). CONCLUSIONS: Key factors associated with no-shows include age, sex, appointment timing, and referral type. These findings can inform targeted interventions to reduce no-show rates and enhance health care delivery in retina clinics.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".