Bibliographic record
Abstract
Physicians treating depression are, on the whole, not entirely satisfied with current antidepressant treatment options. There is a demand for improved therapies with a rapid symptom relief, a high remission rate and greater prevention of recurrence. The remission rate is particularly important since patients with depression not reaching remission have much poorer outcomes than those who do. Several studies have shown that the selective serotonin reuptake inhibitors (SSRIs) and the serotonin and noradrenergic reuptake inhibitors (SNRIs) have higher remission rates than other antidepressant medications. However, data from meta-analyses have suggested that venlafaxine shows higher remission rates than the SSRIs, fluoxetine, fluvoxamine and paroxetine. It is therefore important to examine how the more recently developed SSRIs compare with others in the same drug class and also with SNRIs with regard to symptom relief, remission rate and tolerability. Using data from three pooled studies, the new SSRI escitalopram was compared with the older SSRI citalopram. Escitalopram performed significantly better than citalopram and placebo in terms of percentage responders (defined as a ≥50% decrease of baseline Montgomery and Åsberg Depression Rating Scale [MADRS] score). Furthermore, in a flexible-dose study the remission rate with escitalopram treatment was higher than citalopram during 8 weeks. Remission (defined as MADRS ≤12 at endpoint) in a flexible-dose study was also investigated with escitalopram versus extended release venlafaxine. Overall, the remission rates were similar between the two medications at the end of week 8, but patients on escitalopram achieved sustained remission nearly a week earlier than those on venlafaxine. Higher remission rates were also found with escitalopram compared with venlafaxine in severely ill patients. Furthermore, escitalopram also offered important advantages over venlafaxine with regards to tolerability.
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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 | 0.035 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.021 | 0.030 |
| Insufficient payload (model declined to judge) | 0.038 | 0.023 |
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".