How Effective is Population-Based Cancer Screening? Regression Discontinuity Estimates from the US Guideline Screening Initiation Ages
Bibliographic record
Abstract
We estimate the marginal benefits of population-based cancer screening by comparing cancer test and detection rates on either side of US guideline-recommended initiation ages (age 40 for breast cancer and age 50 for colorectal cancer during the study period). Using a regression discontinuity design and self-reported test data from national health surveys, we find test rates for breast and colorectal cancer increase at the guideline age thresholds by 109% and 78%, respectively. Data from cancer registries in twelve US states indicate that cancer detection rates increase at the same thresholds by 50% and 49%, respectively. We estimate significant effects of screening on earlier breast cancer detection (1.2 cases/1000 screened) at age 40 and colorectal cancer detection (1.1 cases/1000 individuals screened) at age 50. Forty-eight and 73% of the increases in breast and colorectal case detection occur among middle-stage cancers (localized and regional) with most of the remainder among early-stage (in-situ). Our analysis suggests that the cost of detecting an asymptomatic case of breast cancer at age 40 via population-based screening is $107,000-134,000 and that the cost of detecting an asymptomatic case of colorectal cancer at age 50 is $473,000-485,000.
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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 | 0.021 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".