Determination of side effects and medication adherence in major depression patients utilized antidepressants
Bibliographic record
Abstract
The aim of the study is to determine side effects, and medication adherence in patients who were diagnosed with major depression utilized antidepressants. This study was conducted in outpatient psychiatry clinic between February 2013 and May 2013. ‘Toronto Side Effects Scale’ and 4-item Morisky-Green- Levine Medication Adherence Scale were evaluated. Fifty-six major depression patients (mean of age: 40.82 ± 14.28 years old; male/female: 13/43) were included in this study. Patients were treated with escitalopram (46.4%), sertraline (26.8%), venlafaxine (10.7%), citalopram (8.9%) and paroxetine (7.1%). The most common side effects that patients reported were drowsiness/ daytime somnolence (57.1%), dry mouth (46.4%) and increased sleep (46.4%), increased appetite (37.5%) and malaise or fatigue (30.4%). The most severity side effects that patients experienced were drowsiness/ daytime somnolence (25%), increased sleep (19.6%) and decreased libido (14.3%). Only fifteen (26.8%) patients were found high adherent to their medication. It was concluded that although the frequency and severity of side effects have been found high, the percentage of patients’ medication adherence has been obtained low. Patient education and monitoring regarding side effects and medication adherence providing by pharmacists would be contributed to prevent possible drug induced problems in these patients besides the routine services they are taken from outpatient clinic.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".