Non-fatal self-injury after a diagnosis of cancer: A population-based study.
Bibliographic record
Abstract
e18577 Background: Psychological distress is a key construct of patient-centred cancer care. While an increased risk of suicide for cancer patients has been reported, more frequent consequences of distress after a cancer diagnosis, such as non-fatal self-injury (NFSI), remain largely unknown. We examined the risk for NFSI after a cancer diagnosis. Methods: Using linked administrative databases we identified adults diagnosed with cancer between 2007-2019. Cumulative incidence of NFSI, defined as emergency department presentation of self-injury, was computed accounting for the competing-risk of death from all causes. Factors associated with NFSI were assessed using multivariable Fine and Gray models. Results: Of 806,910 included patients, 2,482 had NFSI and 182 died by suicide. 5-year cumulative incidence of NFSI was 0.27% [95%CI 0.25-0.28%]. After adjusting for key confounders, prior severe psychiatric illness whether requiring inpatient care (sub-distribution hazard ratio (sHR) 12.6, [95% CI 10.5-15.2]) or outpatient care (sHR 7.5, 95% CI 6.48-8.84), and prior self-injury (sHR 6.6 [95% CI 5.5-8.0]) were associated with increased risk of NFSI. Young adults (age 18-39) had the highest NFSI rates, relative to individuals >70 (sHR 5.4, [95% CI 4.5-6.5]). The magnitude of association between prior severe psychiatric illness and NFSI was greatest for young adults (interaction term p < 0.01). Certain cancer subsites were also at increased risk, including head and neck (sHR1.52, [95%CI 1.19-1.93]). Conclusions: Patients with cancer have higher incidence of NFSI than suicide after diagnosis. Younger age, prior severe psychiatric illness, and prior self-injury were independently associated with NFSI. These exposures act synergistically, placing young adults with a prior mental health history at greatest risk for NFSI events. Those factors should be used to identify at-risk patients for psycho-social assessment and intervention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".