A systematic review of validated methods for identifying suicide or suicidal ideation using administrative or claims data
Bibliographic record
Abstract
PURPOSE: As part of the Mini-Sentinel pilot program, under contract with the Food and Drug Administration, an effort has been made to evaluate the validity of algorithms useful for identifying health outcomes of interest, including suicide and suicide attempt. METHOD: Literature was reviewed to evaluate how well medical episodes associated with these events could be identified in administrative or claims data sets from the USA or Canada. RESULTS: Six studies were found to include sufficient detail to assess performance characteristics of an algorithm on the basis of International Classification of Diseases, Ninth Revision, E-codes (950-959) for intentional self-injury. Medical records and death registry information were used to validate classification. Sensitivity ranged from 13.8% to 65%, and positive predictive value range from 4.0% to 100%. Study comparisons are difficult to interpret, however, as the studies differed substantially in many important elements, including design, sample, setting, and methods. Although algorithm performance varied widely, two studies located in prepaid medical plans reported that comparisons of database codes to medical charts could achieve good agreement. CONCLUSIONS: Insufficient data exist to support specific recommendations regarding a preferred algorithm, and caution should be exercised in interpreting clinical and pharmacological epidemiological surveillance and research that rely on these codes as measures of suicide-related outcomes.
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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 | 0.018 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".