Predictors of Adherence to Short-Course Probiotics Among Children with Gastroenteritis who are Enrolled in a Clinical Trial
Bibliographic record
Abstract
BACKGROUND: To improve our understanding of adherence to discharge medications in the ED and within research trials, we sought to quantify medication adherence and identify predictors thereof in children with acute gastroenteritis (AGE). METHODS: We conducted a secondary analysis of a randomized trial of twice daily probiotic for 5 days. The population included previously healthy children aged 3-47 months with AGE. The primary outcome was patient-reported adherence to the treatment regimen, defined a priori as having received >70% of the prescribed doses. Secondary outcomes included predictors of treatment adherence and concordance between patient-reported adherence and the returned medication sachet counts. RESULTS: After excluding participants with missing data on adherence, 760 participants were included in this analysis: 383 in the probiotic arm (50.4%); and 377 in the placebo arm (49.6%). Self-reported adherence was similar in both groups (77.0% in probiotic versus 80.3% in placebo). There was good agreement between self-reported adherence and sachet counts (87% within limits of agreement (-2.9 to 3.5 sachets) on the Bland-Altman plots). In the multivariable regression model, covariates associated with adherence were greater number of days of diarrhea post-emergency department visit, and the study site; covariates negatively associated with adherence were age 12-23 months, severe dehydration and greater total number of vomiting and diarrhea episodes after enrolment. CONCLUSIONS: Longer duration of diarrhea and study site were associated with higher probiotic adherence. Age 12-23 months, severe dehydration and greater number of vomiting and diarrhea episodes post enrolment negatively predicted treatment 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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".