Association Between Nonfatal Self-Injury and Survival Following a Cancer Diagnosis: A Comparative Population-Based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Nonfatal self-injury (NFSI) following a cancer diagnosis is a very serious mental health sequalae. Whether NFSI has an impact on patient outcomes is unknown. To help determine the impact and burden of NFSI in cancer care, this study examined the association between NFSI following a cancer diagnosis and subsequent overall survival (OS). METHODS: A retrospective population-based cohort study using linked administrative health care included patients with a new cancer diagnosis in 2007 through 2019. The exposure was NFSI (emergency department visit for self-injury of intentional or undetermined intent), treated as time-varying. The outcome was OS, as death from any cause measured from cancer diagnosis. Extended Cox multivariable models examined the association between NFSI and OS, adjusting for patient, cancer, and mental health history characteristics. RESULTS: Of 806,910 patients, 2,482 (0.31%) had NFSI at a median of 29 months (IQR, 11-57) from cancer diagnosis. Of those, 81 had >1 NFSI event. Patients with NFSI had inferior OS compared with those who had not yet experienced an NFSI (adjusted hazard ratio [HR], 1.73; 95% CI, 1.61-1.85). When treating NFSI as a count variable, there was an association between each additional NFSI and OS (adjusted HR, 1.17; 95% CI, 1.08-1.26). CONCLUSIONS: NFSI following a cancer diagnosis was independently associated with inferior OS. This finding highlights NFSI as a crucial event for patients with cancer and supports the importance of identifying and providing treatment for patients with cancer with cancer-related distress, such as marked by higher risk of NFSI.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".