Determinants of Dropping Out of Remote Patient-Reported Outcome–Based Follow-Up Among Patients With Epilepsy: Prospective Cohort Study
Bibliographic record
Abstract
Background: The use of patient-reported outcome (PRO) measures is an emerging field in health care. In the Central Denmark Region, epilepsy outpatients can participate in remote PRO-based follow-up by completing a questionnaire at home instead of attending a traditional outpatient appointment. This approach aims to encourage patient engagement and is used in approximately half of all epilepsy outpatient consultations. However, dropout in this type of follow-up is a challenging issue. Objective: This study aimed to examine the association between potential self-reported determinants and dropout in remote PRO-based follow-up for patients with epilepsy. Methods: This prospective cohort study (n=2282) explored the association between dropout in remote PRO-based follow-up for patients with epilepsy and 9 potential determinants covering 3 domains: health-related self-management, general and mental health status, and patient satisfaction. The associations were examined using multiple logistic regression analyses with adjustment for sex, age, education, and cohabitation. Results: A total of 770 patients (33.7%) dropped out of remote PRO-based follow-up over 5 years. Statistically significant associations were identified between all potential determinants and dropouts in PRO-based follow-up. Patients with low social support had an odds ratio of 2.20 (95% CI 1.38-3.50) for dropout. Patients with poor health ratings had an odds ratio of 2.17 (95% CI 1.65-2.85) for dropout. Similar estimates were identified for the remaining determinants in question. Conclusions: Patients with reduced self-management, poor health status, and low patient satisfaction had higher odds of dropout in remote PRO-based follow-up. However, further research is needed to determine the reasons for dropout.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".