Comorbid Illness, Bowel Preparation, and Logistical Constraints Are Key Reasons for Outpatient Colonoscopy Nonattendance
Bibliographic record
Abstract
Background. Colonoscopy nonattendance is a challenge for outpatient clinics globally. Absenteeism results in a potential delay in disease diagnosis and loss of hospital resources. This study aims to determine reasons for colonoscopy nonattendance from a Canadian perspective. Design. Demographic data, reasons for nonattendance, and patient suggestions for improving compliance were elicited from 49 out of 144 eligible study participants via telephone questionnaire. The 49 nonattenders were compared to age and sex matched controls for several potential contributing factors. Results. Nonattendance rates were significantly higher in winter months; the OR of nonattendance was 5.2 (95% CI, 1.6 to 17.0, p < 0.001) in winter versus other months. Being married was positively associated with attendance. There was no significant association between nonattendance and any of the other variables examined. The top 3 reasons for nonattendance were being too unwell to attend the procedure, being unable to complete bowel preparation, or experiencing logistical challenges. Conclusions. Colonoscopy attendance rates appear to vary significantly by season and it may be beneficial to book more colonoscopies in the summer or overbook in the winter. Targets for intervention include more tailored teaching sessions, reminders, taxi chits, and developing a hospital specific colonoscopy video regarding procedure and bowel preparation requirements.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".