Short‐term cancer mortality projections: A comparative study of prediction methods
Bibliographic record
Abstract
This paper provides a systematic comparison of cancer mortality and incidence projection methods used at major national health agencies. These methods include Poisson regression using an age-period-cohort model as well as a simple log-linear trend, a joinpoint technique, which accounts for sharp changes, autoregressive time series and state-space models. We assess and compare the reliability of these projection methods by using Canadian cancer mortality data for 12 cancer sites at both the national and regional levels. Cancer sites were chosen to provide a wide range of mortality frequencies. We explore specific techniques for small case counts and for overall national-level projections based on regional-level data. No single method is omnibus in terms of superior performance across a wide range of cancer sites and for all sizes of populations. However, the procedures based on age-period-cohort models used by the Association of the Nordic Cancer Registries tend to provide better performance than the other methods considered. The exception is when case counts are small, where the average of the observed counts over the recent 5-year period yields better predictions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".