Bibliographic record
Abstract
Osazuwa-Peters et al highlight the importance of suicide as a competing risk for patients with head and neck cancer. I appreciate the opportunity to address this topic. The authors cite work from Kendal, reporting US SEER data from patients with cancer diagnoses between 1973 and 2001. The frequency of completed suicide was 0.05% in females and 0.32% in males with head and neck cancer. In contrast, our own data from The Princess Margaret Cancer Centre (Toronto, Canada), show two known and one suspected completed suicide among 7,550 patients with head and neck cancer (69%male) diagnosed between 2003 and 2013, for a rate of approximately 0.03% to 0.04%. We can only speculate whether universal health care, province-wide cancer distress screening, and other supports, provided within and without our Head and Neck Cancer Survivorship Program, contribute to the different rates seen between countries and during a time span of more than a decade. Do three deaths during a decade warrant a comprehensive suicide-prevention strategy? Although tragic, from a population perspective such an initiative is unlikely to be cost effective. Suicide in patients with cancer may often (but not always) reflect inadequate recognition of, or support for, psychosocial concerns. Unquestionably, longstanding physical impairments and associated disability contribute to psychosocial health. Efforts to improve rehabilitation, distress screening, community support, and psychosocial services for patients and family members in need should indeed be incorporated into survivorship care strategies. Future research evaluating such approaches could include cause of death as well as quality-of-life end points.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.030 | 0.038 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".