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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".