All-Cause and Cancer-Specific Death of Older Adults Following Surgery for Cancer
Bibliographic record
Abstract
Importance: Cancer care has inherent complexities in older adults, including balancing risks of cancer and noncancer death. A poor understanding of cause-specific outcomes may lead to overtreatment and undertreatment. Objective: To examine all-cause and cancer-specific death throughout 5 years for older adults after cancer resection. Design, Setting, and Participants: This population-based cohort study was conducted in Ontario, Canada, using the administrative databases stored at ICES (formerly the Institute for Clinical Evaluative Sciences). All adults 70 years or older who underwent resection for a new diagnosis of cancer between January 1, 2007, and December 31, 2017, were included. Patients were followed up until death or censored at date of last contact of December 31, 2018. Exposures: Cancer resection. Main Outcome and Measures: Using a competing risks approach, the cumulative incidence of cancer and noncancer death was estimated and stratified by important prognostic factors. Multivariable subdistribution hazard models were fit to explore prognostic factors. Results: Of 82 037 older adults who underwent surgery (all older than 70 years; 52 119 [63.5%] female), 16 900 of 34 044 deaths (49.6%) were cancer related at a median (interquartile range) follow-up of 46 (23-80) months. At 5 years, estimated cumulative incidence of cancer death (20.7%; 95% CI, 20.4%-21.0%) exceeded noncancer death (16.5%; 95% CI, 16.2%-16.8%) among all patients. However, noncancer deaths exceeded cancer deaths starting at 3 years after surgery in breast, prostate, and melanoma skin cancers, patients older than 85 years, and those with frailty. Cancer type, advancing age, and frailty were independently associated with cause-specific death. Conclusions and Relevance: At the population level, the relative burden of cancer deaths exceeds noncancer deaths for older adults selected for surgery. No subgroup had a higher burden of noncancer death early after surgery, even in more vulnerable patients. This cause-specific overall prognosis information should be used for patient counseling, to assess patterns of over- or undertreatment in older adults with cancer at the system level, and to guide targets for system-level improvements to refine selection criteria and perioperative care pathways for older adults with cancer.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".