Correlates of Attempted Suicide from the Emergency Room of 2 General Hospitals in Montreal, Canada
Bibliographic record
Abstract
Introduction: The epidemiology of attempted suicide has not been well characterized because of lack of national data or an International Classification of Diseases (ICD) code for suicide attempts. We conducted a retrospective chart review in 2 adult general hospitals (tertiary and community) in Montreal, Canada, in 2009-2010 to 1) describe the characteristics of men and women who presented to the emergency department (ED) and/or were hospitalized following a suicide attempt, 2) identify factors associated with attempts requiring hospitalizations, and 3) validate the use of International Classification of Diseases, 10th Revision (ICD-10) codes for “intentional self-harm” as a method to detect suicide attempts from hospital abstract summary records. Method: All potential suicide attempts were identified from hospital abstract summary records and ED nursing triage file using ICD-10 codes and keywords suggestive of suicide attempts. All identified charts were examined, and those with confirmed suicide attempts were fully reviewed. Results: Of the 5746 identified charts, 369 were fully reviewed. Of these, 176 were for suicide attempters treated in the ED and 193 for hospitalized attempters, of whom 46% had an ICD-10 code for intentional self-harm. Poisoning (46%) was the most frequent method of suicide used. Half of attempters were younger than 34 years, 53% were female, and 75% had a history of mental disorders. Conclusion: About half of individuals who seek medical care for attempted suicide are admitted to hospital. About half of attempters use poisoning as a method of suicide, and a quarter do not have a history of mental disorders. Intentional self-harm codes capture only about half of hospitalized attempters.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".