Prescription Stimulant Use and Hospitalization for Psychosis or Mania
Bibliographic record
Abstract
Small studies suggest that prescription stimulants can precipitate psychosis and mania. We conducted a population-based case-crossover study to examine whether hospitalization for psychosis or mania was associated with initiation of stimulant therapy. Between October 1, 1999 and March 31, 2013, we studied 12,856 young people who received a stimulant prescription and were subsequently hospitalized for psychosis or mania. Of these, 183 commenced treatment during 1 of 2 prespecified 60-day intervals (defined as the "risk interval" and "control interval," respectively) prior to admission. We found that stimulant initiation was associated with an increased risk of hospitalization for psychosis or mania in the subsequent 60 days (odds ratio, 1.86; 95% confidence interval, 1.39-2.56). The risk was marginally higher in patients treated with antipsychotic drugs (odds ratio, 2.06; 95% confidence interval, 1.38-3.28), but remained in patients with no such history (odds ratio, 1.66; 95% confidence interval, 1.09-2.66). One third of subjects received another stimulant prescription after hospital discharge. Of these, 45% were readmitted with psychosis or mania shortly thereafter. We conclude that initiation of prescription stimulants is associated with an increased risk of hospitalization for psychosis or mania. Resumption of therapy is common, which may reflect a lack of awareness of the potential causative role of these drugs.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".