Bibliographic record
Abstract
BACKGROUND: Certain drugs may contribute to the etiology of depressive symptoms and depressive disorders. The objective of this review is to critically appraise the literature concerned with these potential etiological associations. METHOD: The review was based on papers uncovered in electronic literature searches using Medline, Psychlit and Psychological Abstracts. Statistical power calculations were used to assist in the interpretation of negative results. RESULTS: A large number of publications were uncovered, but most of these were case reports. There were relatively few empirical studies. Corticosteroids, certain calcium channel blockers and digoxin have been associated with depression by replicated, well conducted studies. Psychostimulant withdrawal is also associated with prominent depressive symptoms. Preliminary evidence suggests that antihyperlipidemic agents, angiotensin converting enzyme inhibitors, sedative hypnotics, psychostimulants and certain hormonal agents may also cause depression. Despite an extensive literature, the potential association between beta-blockers and depressive symptoms remains controversial. There is no substantial evidence that l-dopa or histamine-2-receptor blockers cause depression and the literature is relatively conclusive in determining that thiazide diuretics are not associated with depressive symptoms. CONCLUSIONS: A small, but growing, literature confirms that certain drug exposures can contribute to the biopsychosocial etiology of depressive symptoms and disorders. Current beliefs and diagnostic conventions classify drug-induced depression into a distinct category (Substance-Induced Mood Disorder): but this approach is not specifically supported by the existing literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".