Patient Characteristics Associated with Nonprescription Drug Use in Intentional Overdose
Bibliographic record
Abstract
OBJECTIVE: Over-the-counter (OTC) medications remain freely available to suicidal patients, despite their potential lethality and common use in suicide. The study's main objective was to identify patient characteristics, particularly psychiatric diagnosis associated with the use of OTC medications in intentional overdose. METHODS: We retrospectively reviewed 95 charts from patients who presented to St Paul's Hospital from August 1, 1997, to July 31, 1998, with a discharge diagnosis of intentional drug overdose. Univariate analysis was carried out to identify potential risk markers for OTC medication use, and logistic regression was performed using these variables. RESULTS: When the variables age, sex, and concurrent psychiatric diagnoses were controlled, use of OTC medications in overdose was significantly lower in patients with a DSM-IV diagnosis of substance abuse (OR 0.11, P = 0.005) and in those who possessed prescription medications at the time of overdose (OR 0.18, P = 0.007). Most patients in this cohort (82%) had at least 1 of these 2 traits. Although not statistically significant, younger patients appeared more likely to choose OTC medications for overdose. CONCLUSION: Suicide-prone patients with a diagnosis of substance abuse and who possess prescription medications are unlikely to use OTC medications in overdose. For this cohort, this represents a relatively small proportion of patients whom clinicians should consider to be at greater risk for attempting suicide when using OTC medication, especially acetaminophen.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".