Measuring Mortality Reductions in Cancer Screening Trials
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Randomized trials involving large numbers of people and long follow-up have helped measure the mortality reductions achievable by screening for cancer. However, in many of these trials, the reported reductions have been modest. Part of the reason is the inappropriate way the reductions have been calculated. Analyses have largely ignored the fact that there is a time window in the first several years after screening begins in which there cannot be a sizable mortality reduction, followed by one in which the reductions become evident, and-unless screening is continued-a third window in which mortality rates in the screened group revert to those in the unscreened group. This review uses time-specific mortality ratios to address the timing and extent of the reductions achieved in trials of screening for prostate, breast, and colorectal cancer. The author finds that the mortality reductions reported in the literature have substantially underestimated what might be accomplished with continued screening. The natural history of the disease, the frequency of screening, and the duration of follow-up determine the time patterns in the reductions observed in trials. Without appropriate analyses, results from cancer screening trials will be distorted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 it