Evidence for reducing cancer-specific mortality due to screening for breast cancer in Europe: A systematic review
Bibliographic record
Abstract
Background: The aim of this study was to quantify the impact of organised mammography screening on breast cancer mortality across European regions. Therefore, a systematic review was performed including different types of studies from all European regions and stringently used clearly defined quality appraisal to summarise the best evidence. Methods: Six databases were searched including Embase, Medline and Web of Science from inception to March 2018. To identify all eligible studies which assessed the effect of organised screening on breast cancer mortality, two reviewers independently applied predefined inclusion and exclusion criteria. Original studies in English with a minimum follow-up of five years that were randomised controlled trials (RCTs) or observational studies were included. The Cochrane risk of bias instrument and the Newcastle–Ottawa Scale were used to assess the risk of bias. Results: Of the 5015 references initially retrieved, 60 were included in the final analysis. Those comprised 36 cohort studies, 17 case–control studies and 7 RCTs. None were from Eastern Europe. The quality of the included studies varied: Nineteen of these studies were of very good or good quality. Of those, the reduction in breast cancer mortality in attenders versus non-attenders ranged between 33% and 43% (Northern Europe), 43%–45% (Southern Europe) and 12%–58% (Western Europe). The estimates ranged between 4% and 31% in invited versus non-invited. Conclusion: This systematic review provides evidence that organised screening reduces breast cancer mortality in all European regions whe
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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 | 0.015 | 0.064 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".