Patient Nonadherence To Medication in Inflammatory Bowel Disease
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to identify determinants of nonadherence to medication in outpatients with established inflammatory bowel disease (IBD). METHODS: Ten gastroenterologists and 153 of their IBD patients participated in this prospective study. Demographic, clinical, and psychosocial characteristics, as well as patient-physician discordance, were assessed at an office visit. Nonadherence to medication was assessed 2 wk later. Separate generalized estimating equations were used to identify determinants of nonadherence. RESULTS: Physicians averaged 47.9 yr in age (range 30.1-57.5 yr), and 90% were male. Patients averaged 37.0 yr (SD = 15.1), and 87 (56.9%) were female. In all, 63 patients (41.2%) were nonadherent to medication; of these, 51 (81.0%) indicated unintentional nonadherence, 23 (36.5%) intentional nonadherence, and 11 (17.5%) both. Overall nonadherence was predicted by disease activity (OR = 0.55, p = 0.0022), new patient status (OR = 2.14, p = 0.0394), disease duration (OR = 0.50, p = 0.0001), and scheduling a follow-up appointment (OR = 0.30, p = 0.0059), whereas higher discordance on well-being was predictive only in psychologically nondistressed patients (p = 0.0026 for interaction). Unintentional nonadherence was predicted by age (OR = 0.97, p = 0.0072), new patient status (OR = 2.80, p = 0239), and higher discordance on well-being in psychologically nondistressed patients (p = 0.0504). Intentional nonadherence was predicted by disease duration (OR = 0.55, p = 0032), scheduling a follow-up appointment (OR = 0.12, p = 0.0001), certainty that medication would be helpful (OR = 0.99, p = 0.0409), and total patient-physician discordance (OR = 1.59, p =.0120). CONCLUSIONS: These findings suggest that the therapeutic relationship, as well as individual clinical and psychosocial characteristics, influence adherence to medication.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".