Bibliographic record
Abstract
BACKGROUND: Appropriate statistical analysis is required to measure the impact of early detection and treatment of cancer. The current practice of using cumulative mortality ignores both (1) the delay between early treatment and the time that any averted deaths would have otherwise occurred, and (2) cessation of these delayed benefits some time after screening is discontinued. METHODS: We use time-specific mortality density ratios to estimate the mortality ratio in the "window of influence." We then use time-specific incidence density ratios to assess the extent to which the removal of polyps and other possibly precancerous lesions detected by fecal occult blood screening reduces the incidence of colorectal cancer. RESULTS: Applied to a theoretical example, the current practice of using cumulative mortality substantially underestimates the reduction in mortality achievable by early treatment. If there is sufficient time for the full impact to emerge, time-specific mortality patterns provide a more accurate measure. In a previous analysis of the screening study, the reduction in cumulative incidence in the screened groups was just under 20%. In our reanalysis, yearly incidence density ratios indicate that had screening not been interrupted, there might have been a 40% reduction in incidence. CONCLUSIONS: Time-specific mortality ratios provide a more sensitive measure of the effects of early detection and treatment. Measures based on cumulative mortality are diluted by inclusion of deaths that occur soon after the initiation of screening as well as deaths that occur too long after the cessation of screening.
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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 | 0.024 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".