Patterns of co-morbidity in male suicide completers
Bibliographic record
Abstract
BACKGROUND: Psychiatric co-morbidity is thought to be an important problem in suicide, but it has been little investigated. This study aims to investigate patterns of co-morbidity in a group of male suicide completers. METHOD: One hundred and fifteen male suicide completers from the Greater Montreal Area and 82 matched community controls were assessed using proxy-based diagnostic interviews. Patterns of co-morbidity were investigated using latent class analysis. RESULTS: Three subgroups of male suicide completers were identified (L2 = 171.62, df = 2012, P < 0.05). they differed significantly in the amount of co-morbidity (Kruskal-Wallis chi2 = 71.227, df = 2. P < 0.000) and exhibited different diagnostic profiles. Co-morbidity was particularly found in subjects with disorders characterized by impulsive and impulsive-aggressive traits, whereas subjects without those traits had levels of co-morbidity which were not significantly different from those of controls (chi2 = 8.17, df = 4, P = 0.086). CONCLUSIONS: Suicide completers can be divided into at least three subgroups according to co-morbidity: a low co-morbidity group, a substance-dependent group and a group exhibiting childhood onset of psychopathology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".