Screening Sensitivity and Sojourn Time From Breast Cancer Early Detection Clinical Trials: Mammograms and Physical Examinations
Bibliographic record
Abstract
PURPOSE: To estimate sensitivities of breast cancer screening modalities and preclinical duration of the disease from eight breast cancer screening clinical trials. PATIENTS AND METHODS: Screening programs invariably lead to diagnosis of disease before signs or symptoms are present. Two key quantities of screening programs are the sensitivity of the disease detection modality and the mean sojourn time (MST). The observed screening histories in a periodically screened cohort make it possible to estimate these quantities of interest. We applied recently developed statistical methods to data from eight randomized breast cancer screening trials to estimate the sensitivities of early detection modalities and MST. Moreover, when a screening trial involved two screening modalities, our methods enabled the estimation of the individual sensitivity of each screening modality. RESULTS: We analyzed breast cancer data from several screening trials and have relatively complete data from the Health Insurance Plan (HIP), Edinburgh, and two Canadian studies. The screening sensitivity for mammography, physical examination, and MST were, respectively, HIP: 0.39, 0.47, and 2.5 years; Edinburgh: 0.63, 0.40, and 4.3 years; Canadian (age 40 to 49 at entry): 0.61, 0.59, and 1.9 years; Canadian (age 50 to 59 at entry): 0.66, 0.39, and 3.1 years. CONCLUSION: The public debate on early breast cancer detection is mainly centered on mammograms. However, the current study indicates that a physical examination is of comparable importance. Cautious interpretation of trial differences is required as a result of various experimental designs and the age dependency of screening sensitivity and MST.
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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 | 0.230 | 0.486 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".