Physician Survey Regarding Patient Nonattendance at Follow-up Appointments at a University-Affiliated Medical Dermatology Clinic
Bibliographic record
Abstract
BACKGROUND: Patient nonattendance is a frequent occurrence in dermatology clinics, and our responsibility regarding the follow-up of these patients remains nebulous. OBJECTIVE: This study sought to evaluate the beliefs and practices of physicians at a university-affiliated medical dermatology clinic regarding patient nonattendance at follow-up appointments and to provide an algorithm to deal appropriately with absentee patients based on various Canadian medical association guidelines. METHODS: A questionnaire was distributed to the 17 dermatologists practicing at the Centre Hospitalier de l'Université de Montréal medical dermatology clinic. We contacted provincial and national medical associations regarding directives for patient follow-up. RESULTS: There is a lack of consensus among dermatologists at the Centre Hospitalier de l'Université de Montréal regarding responsibility toward patients who miss their follow-up appointments. However, the majority of survey respondents consider that patient follow-up must be adjusted on a case-by-case basis and that diagnoses at risk for high morbidity and mortality require particular attention, which is in line with various Canadian medical association guidelines. CONCLUSION: Dermatologists should have a structured approach to dealing with patients who miss their follow-up appointments to ensure the appropriate care of all patients.
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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 | 0.003 | 0.013 |
| 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) | 0.003 | 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".