Pediatric Suicide Attempt Non-Disclosure: an Analysis of Discrepant Screening Results
Bibliographic record
Abstract
The Ask-Suicide Screening Questions (ASQ) is a validated tool developed to assess suicidal risk in pediatric medical settings with one item assessing historical attempts. While the psychometric properties of the ASQ are well-established, little is known about how youth respond to this question upon repeated administrations. We conducted a retrospective analysis of electronic medical record data by youth who received the ASQ from December 2019 to November 2023 at an urban academic children's hospital. Youth who disclosed a suicide attempt but denied an attempt history at a subsequent visit were identified. Multivariate regression and manual chart review were utilized to identify demographic and clinical variables related to non-disclosure of a previously disclosed attempt. Of 1861 encounters (1460 unique patients) with a disclosed historic suicide attempt, re-screening occurred in 503 future encounters. One hundred forty instances of nondisclosure occurred (127 unique patients). Encounters were classified into false positives (N = 26), encounters where nondisclosure by patients did not impact clinical response (N = 40), and encounters where nondisclosure resulted in no further suicide risk assessment (N = 74). Of this last group, 47.3% received no risk assessment at the initial visit. Compared to the initial visit, the nondisclosure visit was more likely to have a medical presenting complaint and to have negative responses on ASQ questions related to recent suicidal ideation. Denial of a historic attempt upon repeat administration of the ASQ is not uncommon among pediatric patients, and this is more likely to occur at an encounter for a medical presenting complaint.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".