Ninety-three cases of alcohol dependence following SSRI treatment
Bibliographic record
Abstract
BACKGROUND: There have been recent reports linking serotonin reuptake inhibitor use with increased alcohol consumption. A syndrome of alcoholism precipitated by a common treatment has clear implications for both research and treatment if it is a common phenomenon. OBJECTIVE: To explore the profile of people affected, and drugs that might trigger the syndrome. METHODS: We have selected reports to RxISK.org reporting the problem and cases linked to a blog posting outlining the syndrome and mined these for data on age, gender, drug of use, pattern of outcome on treatment, and impact of the problem. RESULTS: The data make it clear that all treatments with significant effects on the serotonin reuptake system are likely to cause this problem. Both sexes, and all ages are affected and reports have come from a range of countries. While stopping treatment can lead to the problem clearing, a failure to stop can result in death. CONCLUSIONS: SSRI induced alcoholism is likely to be a relatively common problem. Recognizing the problem can lead to a gratifying cure. A failure to recognize it can be fatal.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".