Projecting the yearly mortality reductions due to a cancer screening programme
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
The decision on whether to implement a 20-year screening programme for a cancer requires weighing the harms and costs against the health benefits (such as the number of cancer deaths averted every year). The evidence of the benefits is often based on a single-number summary, such as the mortality reduction over the entire follow-up time in a single trial, or an average of such one-number measures from a meta-analysis of several trials. There are several problems associated with using the traditional one-number summaries from trials to deduce the yearly mortality reductions expected from a sustained screening programme. We here propose using a rate ratio curve, and its complement (a mortality reduction curve), to address the mortality impact (timing, magnitude, and duration) of a screening programme. This curve is easy to interpret, as it shows when mortality reductions begin, how big they are, and how long they last. We illustrate when and how such rate ratio curves from screening trials could be computed, and how they could be used to compare reduction patterns expected with different screening regimens. We encourage trialists to report the necessary data to arrive at such projections.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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