No Show: Incidence of Nonattendance at a Dermatology Practice in a Single Universal Payer Model
Bibliographic record
Abstract
BACKGROUND: Nonattendance at scheduled appointments is a major problem. Previous studies have shown rates between 17 and 31%. Most US studies found the type of payer to be the greatest determinant of attendance rates. OBJECTIVES: This study examines the no-show rate in a private dermatology practice under a single universal payer model, including the effects of old versus new patient, gender, day of the week, month, and weather. RESULTS: The overall rate of nonattendance was lower than in all previous studies (7.79%), with the only statistically significant variable being established versus new patients. LIMITATIONS: Certain demographic data investigated in previous studies (eg, age, socioeconomic status) were not assessable. Data are from a single office. CONCLUSION: The no-show rate in a single universal payer, private practice model is low, especially for established patients.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".