Emergency department and inpatient coding for self‐harm and suicide attempts: Validation using clinician assessment data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Administrative data have been used to determine the occurrence of suicide attempts and deliberate self-harm, but research about the accuracy of these sources is limited. This study used a clinical sample (n = 5719) containing psychiatry consultations from the emergency departments and inpatient units of the two major tertiary hospitals in Winnipeg, Canada to validate the accuracy of inpatient hospital diagnosis codes at identifying presentations for self-harm and suicide attempts. The Columbia Classification Algorithm of Suicide Assessment (C-CASA) was used as the gold standard. International Classification of Diseases version 10 Canadian Enhancement codes for intentional self-harm, undetermined intent self-harm, and accidental poisoning were assessed. Measures of validity included Kappa (κ), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Sensitivity of hospitalized attempts was low using intentional intent codes (36.9%, 95% confidence interval [CI]: 32.4-41.4%) but improved using unknown intent and accidental poisoning codes (44.8%, 95% CI: 40.2-49.4%). Agreement for suicide attempts did not increase with the addition of unknown intent and accidental poisoning codes (κ = 0.465-0.481), but were better for any self-harm (κ = 0.395-0.478). Hospital diagnosis codes undercount attempts and self-harm admissions. Including more data sources might improve the detection of events.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it