A Medicare-Associated Spike in U.S. Cancer Rates at Age 65, 2000–2010
Bibliographic record
Abstract
Age 65 represents a transition point where most U.S. residents begin Medicare coverage. We examined whether or not delays in medical care near this age extend to cancer diagnosis. We calculated single-year-of-age cancer incidence rates by site and stage for the most common cancer sites (i.e., prostate, female breast, lung, and colorectal) for the 2000-2010 period using data from the SEER 18 registries, and we used Poisson regression to identify a possible age-65 effect. The analysis was repeated on comparable Canadian data. Cancer rates at age 65 were found to be as much as 15% above expected in the U.S. data, with the age-65 effect strongly associated with site- and stage-specific survival. A smaller association was seen in the Canadian data. We found strong evidence that diagnosis of less severe cancers spikes at age 65. Delay of medical care prior to this age has complex policy implications.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".