Persistence and compliance to antidepressant treatment in patients with depression: A chart review
Bibliographic record
Abstract
BACKGROUND: Adherence has recently been suggested to be divided into these two components: persistence (i.e., whether patients continue treatment or not) and compliance (i.e., whether patients take doses as instructed). However, no study has yet assessed these two clinically relevant components at the same time in adherence to antidepressant treatment in the clinical outpatient setting. METHODS: In this retrospective chart-review, 6-month adherence to antidepressants was examined in 367 outpatients with a major depressive disorder (ICD-10) (170 males; mean +/- SD age 37.6 +/- 13.9 years), who started antidepressant treatment from April 2006 through March 2007. Additionally, we evaluated Medication Possession Rate (MPR), defined as the total days a medication was dispensed to patients divided by the treatment period. RESULTS: Only 161 patients (44.3%) continued antidepressant treatment for 6 months. Among 252 patients who discontinued their initial antidepressant, 63.1% of these patients did so without consulting their physicians. Sertraline use was associated with a higher persistence rate at month 6 (odds ratio 2.59 in comparison with sulpiride), and the use of anxiolytic benzodiazepines had a positive effect on persistence to antidepressant treatment only at month 1 (odds ratio 2.14). An overall MPR was 0.77; 55.6% of patients were considered compliant (i.e., a MPR of > or = 0.8). CONCLUSION: Given a high rate of antidepressant discontinuation without consulting their physicians, closer communication between patients and their physicians should be encouraged. Although the use of anxiolytic benzodiazepines was associated with a higher persistence to antidepressant treatment at month 1, the use of these drugs should be avoided as a rule, given their well-known serious adverse effects.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".