Refining the suicide phenotype : psychopathological and familial studies
Bibliographic record
Abstract
Suicide is a serious problem in our society with a high emotional, as well as financial, burden. Research has identified a number of risk factors for suicidal behaviour, including the presence of psychiatric diagnosis, and the comorbidity of psychiatric diagnosis. In particular, high lifetime aggression and impulsiveness have repeatedly been implicated as risk factors for suicidal behaviour, and have also been observed to cluster in families. This study investigates the phenotype of suicide completion through exploration of comorbid patterns of psychopathology and seasonality in order to gain a better understanding of possible subgroups of suicide completers, particularly with respect to impulsive-aggressive behaviours and their psychopathological correlates. This study also explores the familiality of suicidal behaviour, and its relationship to impulsive-aggressive behaviours and their psychopathological correlates. Our findings show that suicide cases can be clustered into three different groups according comorbidity: a low-comorbidity group, a substance-dependent group, and a group exhibiting childhood onset of psychopathology. We also find that seasonal variation in suicide varies according to psychopathology. Finally, we confirm that suicide has a familial component independent of psychopathology, and find evidence to suggest that this may be mediated by severity of suicidal ideation, and aggressive behaviour.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".