Five-year cancer survival by stage at diagnosis in Canada
Bibliographic record
Abstract
Background: Cancer survival estimates provide insights into the effectiveness of early detection and treatment. The stage of cancer at diagnosis is an important determinant of survival, reflecting the extent and spread at the time of disease detection. This work provides stage-specific, five-year survival results not previously available for Canada. Data and methods: Data reflect the population-based Canadian Cancer Registry death-linked analytic file covering the period from 2010 to 2017. The stage at diagnosis was determined by the Collaborative Stage Data Collection System. Five-year net survival (NS) estimates for Canada excluding Quebec were derived using the Pohar Perme estimator for the five most commonly diagnosed cancers. Results: Except for prostate cancer, NS decreased monotonically with increased stage at diagnosis. For example, female breast cancer NS estimates were 100% (stage I), 92% (stage II), 74% (stage III) and 23% (stage IV). Apart from lung cancer, stage I NS exceeded 90% for all cancers studied. The largest sex-specific difference in NS was for lung cancer stage I (female 66%; male 56%). Stage-specific NS generally decreased with age, particularly for early-stage lung cancer. Between the 2010-to-2012 and 2015-to-2017 periods, NS improved among stage IV prostate, female breast and lung cancer cases, as well as for stage I and III lung cancer cases; however, it did not improve at any stage for colon or rectal cancer cases. Interpretation: The work highlights the importance of detecting cancer early, when treatment is most effective. It demonstrates some progress in stage-specific survival among top cancers in Canada and offers data to inform health policy, including screening, and clinical decisions regarding cancer treatment.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".