A Comparison Study of Multiple Measures of Adherence to Antipsychotic Medication in First-Episode Psychosis
Bibliographic record
Abstract
This study evaluates how much agreement there is between subjective reports of adherence to antipsychotic medication and objective or derived measures of adherence in first-episode psychosis (FEP) and asks if any adherence measure could approximate a gold standard based on correlation to symptom improvement in the early phase of treatment. Adherence was assessed in 81 FEP subjects on a monthly basis by reports from patients, clinicians, family, and pill counting. A consensus measure of adherence was derived from all available sources of adherence data. Symptoms were measured using the Positive and Negative Syndrome Scale at study entry and 3 months subsequently. Adherence as measured by patient report, pill count, and clinician report were in good agreement with each other (intraclass correlation coefficient = 0.84), and all of these measures were highly correlated to consensus adherence (r values between 0.86 and 0.98). Mean adherence was slightly higher as rated by patients (83% full doses taken per month) and family members (91%) than by clinicians (76%), pill counting (73%), or consensus value (74%). Early in treatment, each measure of adherence (except family report) was significantly associated with positive symptom reduction, although the order of magnitude of this correlation was greater for pill count and consensus adherence (P < 0.01) compared with patient- or clinician-reported adherence (P < 0.05). Patient or clinician reports provide a reasonable estimate of medication adherence in FEP, but introducing pill counting or a derived measure of adherence may allow more accurate measurement.
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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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".