Identification of hospitalizations for intentional self‐harm when E‐codes are incompletely recorded
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
CONTEXT: Suicidal behavior has gained attention as an adverse outcome of prescription drug use. Hospitalizations for intentional self-harm, including suicide, can be identified in administrative claims databases using external cause of injury codes (E-codes). However, rates of E-code completeness in US government and commercial claims databases are low due to issues with hospital billing software. OBJECTIVE: To develop an algorithm to identify intentional self-harm hospitalizations using recorded injury and psychiatric diagnosis codes in the absence of E-code reporting. METHODS: We sampled hospitalizations with an injury diagnosis (ICD-9 800-995) from two databases with high rates of E-coding completeness: 1999-2001 British Columbia, Canada data and the 2004 US Nationwide Inpatient Sample. Our gold standard for intentional self-harm was a diagnosis of E950-E958. We constructed algorithms to identify these hospitalizations using information on type of injury and presence of specific psychiatric diagnoses. RESULTS: The algorithm that identified intentional self-harm hospitalizations with high sensitivity and specificity was a diagnosis of poisoning, toxic effects, open wound to elbow, wrist, or forearm, or asphyxiation; plus a diagnosis of depression, mania, personality disorder, psychotic disorder, or adjustment reaction. This had a sensitivity of 63%, specificity of 99% and positive predictive value (PPV) of 86% in the Canadian database. Values in the US data were 74, 98, and 73%. PPV was highest (80%) in patients under 25 and lowest those over 65 (44%). CONCLUSIONS: The proposed algorithm may be useful for researchers attempting to study intentional self-harm in claims databases with incomplete E-code reporting, especially among younger populations.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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