Long term treatment of depression with selective serotonin reuptake inhibitors and newer antidepressants
Bibliographic record
Abstract
#### Summary points The introduction of fluoxetine in 1987 marked the arrival of the selective serotonin reuptake inhibitors (SSRIs) for the treatment for depression. In the United States antidepressant prescriptions accounted for 2.6% of primary care visits in 1989, rising to 7.1% in 2000.1 This pattern was reflected across Europe, where SSRIs are now the most commonly prescribed antidepressants. Newer antidepressants, such as the serotonin norepinephrine reuptake inhibitors, including venlafaxine and duloxetine, also contributed to the remarkable increase in antidepressant prescribing.2 Several reasons were put forward for this trend: improved tolerability; reduced lethality in overdose; the aggressive marketing of newly patented drugs; the wider range of available antidepressants; and ease of prescription. An analysis of the UK general practice research database showed that prescriptions almost doubled from 1993 to 2005.3 However, the increase in antidepressant prescribing was not accounted for by new diagnoses but rather a rise in the number of prescriptions given for long term treatment: although the proportion of patients receiving short term treatment declined, the proportion of patients receiving continuing prescriptions for over five years increased. Another primary care study found that …
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".