Participant Preference for Email Follow-up Associated with Adherence to Follow-up: Results of a Longitudinal Study of Minor Adverse Events Post-colonoscopy
Bibliographic record
Abstract
Purpose: Patient adherence to follow-up in a longitudinal study can be problematic. The purpose of this study was to determine the relationship between patient characteristics and adherence to follow-up among individuals who consented to participate in a study of minor adverse events following colonoscopy. Methods: A longitudinal study with follow ups at 2, 14, and 30 days was conducted of individuals (aged 40-75) undergoing colonoscopy at one outpatient endoscopy clinic in Montreal, Canada. Recruitment occurred in the endoscopy waiting area prior to colonoscopy. Baseline data included age, sex, recent symptoms (abdominal pain, bloating, nausea/vomiting, diarrhea, constipation, blood in the stools, anal/rectal pain, headache, other), comorbidity (diabetes, heart disease, lung disease, kidney disease, neurological condition, inflammatory bowel disease), regular use of medication (aspirin, clopidogrel, warfarin, dabigantran, ticagrelor, prasugrel, NSAIDs), and preferred method of follow-up (telephone, email). Adherence to all follows-up was dichotomous and defined as completing the day 2, 14, and 30 follow-ups. Multivariate logistic regression was used to determine the factors related to adherence to all follow-ups. Results: Of 682 eligible individuals, 421 (61.7%) consented to participate (mean age=58.4, 45.1% female). Of these participants, 238 (56.5%) had recent symptoms, 95 (22.6%) had at least one comorbidity, and 90 (21.4%) reported regular medication use. Response rates for the day 2, 14 and 30 follow-ups were 82.7%, 80.8% and 74.8%, respectively, and 64.5% of participants responded to all three follow-ups. Multivariable analysis showed that females (OR 2.15, 95% CI 1.42-3.28) and email followup preference (OR 1.68, 95% CI 1. 90-2.58) were associated with adherence to all follow-ups, while age, comorbidity and regular medication use were not. Conclusion: Females and email follow-up preference were significant predictors of adherence to all follow-ups. Further research is required to determine whether giving participants their preferred method of follow-up or the email method of follow-up was associated with adherence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".