Adjusting relative survival estimates for cancer mortality in the general population.
Bibliographic record
Abstract
BACKGROUND: In theory, expected survival probabilities used in the derivation of relative survival ratios (RSR) are determined from a control group free of the cancer under study. In practice, expected survival is typically estimated from general population life tables--which include people previously diagnosed with cancer--potentially leading to an overestimation of relative survival. DATA AND METHODS: Data are from the Canadian Cancer Registry with mortality follow-up through record linkage to the Canadian Vital Statistics Death Database. Period method RSRs for 2006-to-2008 were derived using general population life tables adjusted for cancer mortality and then compared with estimates derived using corresponding unadjusted life tables. RESULTS: For all cancers combined, the use of general population life tables to derive expected survival probabilities overestimated RSRs by 0.6 (1-year), 2.4 (5-year) and 4.6 (10-year) percentage units. Biases in 5-year survival were highest among males (3.0) and among people aged 75 to 99 at diagnosis (4.1). The bias was negligible for most individual cancers; biases were highest for prostate cancer, followed by colorectal and female breast cancer. INTERPRETATION: Canadian estimates of relative survival for all cancers combined calculated using general life tables warrant adjustment for cancer mortality. Consideration of adjustment for cancer mortality is recommended for estimates of colorectal, female breast and especially prostate 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 | 0.022 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".