Asthma status and suicidal behavior risk: A meta-analysis of cohort studies
Bibliographic record
Abstract
OBJECTIVE: This meta-analysis investigates the differential risks of suicidal behaviors (ideation, attempts, mortality) associated with current asthma and asthma history. METHOD: Retrieve cohort studies on the association between asthma and suicide from PubMed, Embase, and Cochrane library database. Use the Newcastle Ottawa Quality Assessment Scale (NOS) to assess the risk of bias. The risk ratio (RR) of 95% confidence interval (CI) was summarized using a random effects model, and publication bias was evaluated using funnel plots and Egger's trials. RESULT: A total of 12 cohort studies were included and published between 2005 and 2024. The NOS scores for the 12 cohort studies included in this meta-analysis ranged from 7 to 9. Most studies received scores of 7 or 8, indicating a generally high quality. Current asthma conferred a 62% increased risk of suicidal behaviors (RR = 1.62, 95% CI: 1.38-1.88), with suicide attempts showing the strongest association (RR = 2.27, 95% CI: 1.33-3.89). Asthma history was linked only to elevated suicide mortality (RR = 1.87, 95% CI: 1.64-2.14), not non-fatal suicidal behaviors. CONCLUSION: Current asthma status is associated with an increased risk of suicidal behaviors, but a history of asthma correlates only with elevated suicide mortality. This finding highlights the need for proactive mental health screening in asthma management protocols, especially during periods of active disease.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.066 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".