Delineating empirically plausible causal pathways to suicidality among people at clinical high risk for psychosis.
Bibliographic record
Abstract
= 266). Data on correlates of suicidality-including depression and attenuated psychosis symptoms, sleep, and childhood trauma-from two initial study timepoints were submitted to the greedy relaxations of the sparsest permutation algorithm. Intervention calculus was used to estimate the (lower bound) total empirically plausible causal effects of each variable on suicidality. Across both samples, greedy relaxations of the sparsest permutation suggested that symptoms of depression-particularly hopelessness, self-deprecation, and depressed mood-were likely direct causes of suicidality among people at CHR for psychosis. Across samples and measurement time points, intervention calculus indicated that depressed mood exerted the greatest influence over suicidality of all measured variables. This study provides data-driven, testable hypotheses about the causal pathways leading to suicidality among people at CHR for psychosis and suggests promising targets for interventions on suicidality tailored to these individuals. Future experimental research should test these hypotheses by, for example, comparing the suicide risk reduction afforded by interventions aimed at each aforementioned target. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".