Bibliographic record
Abstract
This paper investigates two experimental designs which have been used to evaluate the benefit of the early detection of breast cancer. They have some advantages over a classical design (the screening program versus usual medical care) in that subjects in a control group may benefit by participating in the study. We refer to the two experimental designs as the up-front (UFD) and close-out (COD) designs. The UFD consists of offering an initial exam to all participants. Then they can be randomized to a usual care group or a screening group receiving one or more special examinations. If the outcome of the initial examination is included in the analysis, then the study can answer the question of the benefit of an additional screening program after an initial examination. If the analysis excludes all the cases diagnosed at the initial examination, then the analysis evaluates the benefit of a screening program after elimination of the prevalent cases. These prevalent cases are most likely to be affected by length bias sampling and consequently will tend to have less aggressive disease and live longer. As a result, the UFD can answer two scientific questions. The COD consists of randomizing subjects to a usual care group and a screened group. However, the usual care group receives an examination which coincides at the time of the last exam in the study group. In this paper the power of these two designs have been evaluated. In both cases the power is severely reduced compared to the usual control group receiving no special exams. The power is a function of the sensitivity of the exam, the number and spacings of the exams given to the screened group as well as the sample size, disease incidence of the population and the survival distribution. The theoretical results on power are applied to the Canadian National Breast Cancer Study (ages 40-49) which used an UFD and the Stockholm Mammography Breast Cancer Screening Trial which utilized a COD.
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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 | 0.335 | 0.508 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".