Routine screening for suicidal intention in patients with cancer
Bibliographic record
Abstract
OBJECTIVES: Suicide rates are elevated in individuals with cancer, although suicidal intention is not typically assessed in cancer centers. We evaluated in a large comprehensive cancer center the utility of an electronic Distress Assessment and Response Tool (DART), in which suicidal intention is assessed with a single item. METHODS: Patients attending cancer clinics completed DART as part of routine care. DART includes measures of physical symptoms, depression, anxiety, social difficulties, and practical concerns. Medical variables were obtained from the Princess Margaret Cancer Registry, the data warehouse of cancer patient statistics. A Generalized Estimating Equation (GEE) model was used to assess factors associated with suicidal intention. RESULTS: Between September 2009 and March 2012, 4822/5461 patients (88.3%) who completed DART consented to the use of their data for research. Amongst the latter, 280 (5.9%) of the 4775 patients who answered the question reported suicidal ideation, which was related to physical and psychological distress, and social difficulties (ps < 0.0001). Amongst those with ideation who responded to the intention question, 20/186 (10.8%) reported suicidal intention. Of respondents with more severe suicidal ideation, 12/49 (24.5%) reported suicidal intention. Using a GEE model, suicidal intention in those with ideation was significantly associated with male sex, difficulty making treatment decisions, and with everyday living concerns. CONCLUSIONS: Suicidal ideation is reported on an electronic distress screening tool (DART) by almost 6% of cancer patients, of whom almost 11% report suicidal intention and 33% decline to indicate intention. DART demonstrated utility in identifying patients who may be at highest risk of completed suicide and who require urgent clinical assessment.
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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 | 0.008 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".