Suicidal thoughts and attempts in a transdiagnostic eating disorder sample: Do diagnostic severity criteria predict risk?
Bibliographic record
Abstract
OBJECTIVE: Eating disorders (EDs) are associated with elevated suicide. Low body mass index (BMI) and frequency of purging and binge eating represent severity criteria for EDs and distinguish full-threshold EDs from other specified feeding and eating disorders (OSFED). However, no work has taken a transdiagnostic approach to studying whether severity of these or other features is associated with suicidal ideation (SI) and attempts. METHOD: We examined diagnostic status, ED features, and SI and attempts in a large, transdiagnostic, community sample of 257 women with EDs and 45 controls without a current or past ED in the United States using the EDs Examination interview and the Structured Clinical Interview for the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders). RESULTS: SI and suicide attempts (SA) were elevated in OSFED compared to controls but did not differ between OSFED and full-threshold EDs. Higher BMI predicted increased SI. Number of purging methods, but not frequency, was related to history of SA. Binge episode frequency and size were not significant predictors. CONCLUSIONS: OSFED presents with elevated SI and SA, and ED severity criteria that distinguish OSFED from full-threshold EDs do not predict SI or SA. Suicide risk assessments should be implemented universally across EDs in clinical practice.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".