Bibliographic record
Abstract
BACKGROUND: Cancer survival is influenced by age, comorbidity, and type of cancer. A population-based study was conducted to compare the interplay between age and mortality for different cancers. METHODS: This study analyzed 784,378 cases, comprising 22 of the commonest SEER cancers diagnosed between 1984 and 1993. Competing hazards and proportional hazard analyses for cancer-specific and comorbid death were performed. RESULTS: Median follow-up was up to 159 months, and the median age of diagnosis was 67 years. Cancer-specific and comorbid deaths accumulated most within the first years of diagnosis. With the more biologically aggressive cancers, cancer deaths invariably exceeded comorbid deaths. For the remaining 70% of cancers, comorbidity remained the dominant mode of death. Deaths attributable to both cancer and comorbidity accumulated mostly after the seventh decade of life. Cancer site had a 3-fold greater effect on overall survival than age at diagnosis and a 30-fold effect with cancer-specific survival; age at diagnosis had a 5-fold greater effect on comorbid deaths than site. CONCLUSIONS: Both the age of the affected individual and the biology of the particular cancer have major influences on cancer survival and mode of death. Cancer is largely a disease of the elderly. Within affected individuals, fatalities attributable to cancer and comorbidity appeared inter-related, with cancer-specific deaths dominating for more lethal cancers and comorbid deaths dominating for the remaining majority. For these reasons, further improvements in overall survival may be best anticipated from better geriatric and general medical management as much as from better cancer management.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.010 | 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".