Defining and assessing international classification of disease suicidality phenotypes for genetic studies
Bibliographic record
Abstract
INTRODUCTION: Suicidality, including suicidal ideation (SI), attempt (SA), and death (SD), represents complex and partially overlapping phenotypes that are moderately heritable. Suicidality definition heterogeneity impedes data replication and consolidation efforts by research consortia needed to address the sample size requirements of genetic research. The standardization of suicidality definitions would improve comparability of data across groups but has been insufficiently addressed in existing literature. Here, the Suicide Workgroup of the Psychiatric Genomics Consortium (PGC) provides International Classification of Disease (ICD) definitions and validation in real-world data for SA and SI. METHODS: The PGC Suicide Workgroup used published definitions coupled with expert consensus to develop ICD lists to serve as suicidality phenotype definitions. One SI and two SA lists were produced and evaluated for performance, including via sex stratification, against patient screening responses in multiple independent cohorts (total N = 21,772) with differing ascertainment strategies. RESULTS: ICD code lists for suicidality component definitions were produced. SA ICD lists versus patient responses showed sensitivity of 15.4 % to 71.1 %, specificity of 67.6 % to 96.3 %, and positive predictive values of 0.57-0.92. SI ICD code performance versus patient report also varied in sensitivity (29.4 %-86.1 %), specificity (64.2 % to 90.6 %), and positive predictive values (0.67 to 0.98). CONCLUSIONS: Lists of applicable ICD codes for SI and SA were developed that complied with C-SSRS definitions. Real-world application of ICD codes can vary substantially, perhaps dependent on clinician training and on cohort characteristics. Consistent training in use of ICD codes between sites may improve comparability of data sets.
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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 | 0.026 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".