Mammographic screening: evidence from randomised controlled trials
Bibliographic record
Abstract
BACKGROUND: All randomised breast cancer screening trials have shown a\n reduction in breast cancer mortality in the 'invited for mammography'\n screening arm compared with the 'control arm' for women aged 50 years and\n older at randomisation (overall 25%). However, individually published\n point estimates differ and concern has been raised about methodological\n quality and outcome measures. Materials and Methods Review of the evidence\n on breast cancer mortality reduction and discussion of the causes of\n difference in point estimates in the five Swedish and Canadian trials. A\n summary of the prerequisites for methodological quality and its available\n evidence from the trials is given. Data to support breast cancer mortality\n as a correct outcome measure are presented. RESULTS: There is no reason\n not to use breast cancer mortality as an outcome measure for trials\n intended to reduce breast cancer mortality, both from a clinical and a\n methodological point of view. Everything possible was performed in these\n trials in order to determine this outcome measure as accurately as\n possible. The fact that a few of the trials showed a relatively large\n breast cancer mortality reduction and others far lower reduction rates is\n irrelevant, if one does not consider the background situation in the\n region before the trial started, the design of the trial or quality of\n screening. CONCLUSIONS: There seems no reason to change or halt the\n current nation-wide population-based screening programmes. Nor is there\n any justifiable reason for negative reports towards women or\n professionals.
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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 | 0.084 | 0.389 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.012 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".