Adherence to escitalopram treatment in depression
Bibliographic record
Abstract
Poor adherence to depression treatment is common. Understanding determinants of poor adherence to therapy is crucial to ensure optimal clinical outcomes. The aim of this study was to describe characteristics of dosing history in participants with depression receiving once daily escitalopram. Participants were randomly assigned to interpersonal psychotherapy (IPT) or pharmacotherapy. Participants assigned to IPT who did not evidence a response or remission had escitalopram added to their treatment. Adherence to pharmacotherapy was assessed using an electronically monitored pill cap (MEMS). Fifty-four participants on escitalopram alone and 32 on escitalopram+IPT were monitored. After 200 days, 71.7% of the participants in the escitalopram group and 54.8% of those in the escitalopram+IPT group were still engaged with the dosing regimen. Of those engaged in the dosing regimen, 17.9% (average over 210 days) of the participants did not take their medication (nonexecution). In 69% of the days participants took the correct dosage required. On average, participants had three drug holidays and the mean length of a holiday was 7 days per patient. No difference in adherence patterns was observed between patients receiving escitalopram alone vs. IPT+escitalopram. Early discontinuation of treatment and suboptimal daily execution of the prescribed regimen are the most common facets of poor adherence in this study population.
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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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".