Behavior of self-inflicted violence in patients with bipolar disorder
Bibliographic record
Abstract
BACKGROUND: Studies aimed at understanding the higher risk profiles for self-inflicted violence in individuals with BD become essential as a possible predictive risk measure for the presence of suicidal behavior, corroborating the expressive reduction of suicide deaths in young people who are in psychic suffering. METHODS: The protocol was constructed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA-P) and the research question was constructed using guidelines from the Population Intervention Comparator Outcome Setting (PICOS) strategy. A third reviewer will be contacted, and two studies will be included in the selection, analysis and inclusion phases of the articles, in case of divergence, a third reviewer will be contacted. (1) methodological design studies of cohorts, case-control and cross-sectional; (2) Diagnosis of Bipolar disorder according to Diagnostic and statistical Manual of mental disorders V; (3) Studies with adult population and (4) Studies that consider at least one type of self-inflicted violence as a variable. The articles considered eligible will be analyzed by New Castle - Ottawa quality assessment scale/cross section studies (NOS) to evaluate the quality of the studies. RESULTS: The identification of the characteristics of self-harm may subsidize professionals who work in the treatment of bipolar disorder with greater attention to these practices and monitoring of possible suicidal behaviors. CONCLUSION: This study may represent one of the initial measures of evaluation on these correlations, which will allow to protocol the guidelines in the field of practice and contribute to improvements in public health indexes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.001 | 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".