Suicide Risk Screening and Suicide Prevention in Patients With Cancer
Bibliographic record
Abstract
Abstract Background Suicide rates are up to 4 times greater in cancer compared with the general population, yet best practices for institutional suicide prevention are unknown. The objective of this study was to examine the association between suicide risk screening (SRS), clinician response, and suicide mortality at a comprehensive cancer treatment center. Methods We conducted a naturalistic, retrospective cohort study of patients attending the Princess Margaret Cancer Centre, where routine screening for suicidal intent within the Distress Assessment and Response Tool (DART-SRS) was implemented in 2010. Inverse probability of treatment weighting was used to evaluate the impact of DART-SRS completion on suicide mortality from 2005 to 2014. Chart audits were conducted for clinician response to suicidality, and crude suicide rates over the study period were analyzed. All statistical tests were 2-sided. Results Among 78 650 cancer patients, 89 (0.1%) died by suicide, of whom only 4 (4.5%) had completed DART-SRS. Among DART-SRS completers (n = 14 517), 69 (0.5%) reported suicidal intent, none of whom died by suicide. DART-SRS completion was associated with increased clinician response to suicidality (17.4% vs 6.7%, P = .04), more psychosocial service usage (30.5% vs 18.3%, P < .001), and lower suicide mortality (hazard ratio = 0.29, 95% confidence interval = 0.28 to 0.31). Crude suicide rates at the Princess Margaret Cancer Centre were lower in patients whose first contact year was after DART-SRS implementation. Conclusion DART-SRS completion is associated with lower suicide mortality and increased access to psychosocial care, but patients who did not complete DART-SRS were at highest suicide risk. Further research is needed to identify mechanisms to ensure psychosocial and suicidality assessment in cancer patients who do not complete SRS.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".