Factors associated with patient absenteeism for scheduled endoscopy
Bibliographic record
Abstract
AIM: To identify risk factors to help predict which patients are likely to fail to appear for an endoscopic procedure. METHODS: This was a retrospective, chart review, cohort study in a Canadian, tertiary care, academic, hospital-based endoscopy clinic. Patients included were: those undergoing esophagogastroduodenoscopy, colonoscopy or flexible sigmoidoscopy and patients who failed to appear were compared to a control group. The main outcome measure was a multivariate analysis of factors associated with truancy from scheduled endoscopic procedures. Factors analyzed included gender, age, waiting time, type of procedure, referring physician, distance to hospital, first or subsequent endoscopic procedure or encounter with gastroenterologist, and urgency of the procedure. RESULTS: Two hundred and thirty-four patients did not show up for their scheduled appointment. Compared to a control group, factors statistically significantly associated with truancy in the multivariate analysis were: non-urgent vs urgent procedure (OR 1.62, 95% CI 1.06, 2.450), referred by a specialist vs a family doctor (OR 2.76, 95% CI 1.31, 5.52) and office-based consult prior to endoscopy vs consult and endoscopic procedure during the same appointment (OR 2.24, 95% CI 1.33, 3.78). CONCLUSION: Identifying patients who are not scheduled for same-day consult and endoscopy, those referred by a specialist, and those with non-urgent referrals may help reduce patient truancy.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".