Review: adequately randomised trials showed that mammography screening did not significantly reduce breast cancer, cancer, or all cause mortality but increased breast surgeries
Bibliographic record
Abstract
Gøtzsche PC, Nielsen M. Screening for breast cancer with mammography. Cochrane Database Syst Rev 2006;(4):CD001877.[OpenUrl][1][PubMed][2] Q Does screening for breast cancer with mammography reduce morbidity and mortality? ### ![Graphic][3] Data sources: PubMed (June 2005) and review of reference lists. ### ![Graphic][4] Study selection and assessment: randomised controlled trials (RCTs) that compared mammography screening with no mammography screening in women without previously diagnosed breast cancer. 7 trials met the selection criteria; 2 had adequate randomisation, 4 had sub-optimal randomisation, and 1 was not adequately randomised. Separate analyses were done for adequately and sub-optimally randomised trials. ### ![Graphic][5] Outcomes: breast cancer mortality, cancer mortality, all cause mortality, surgical interventions, and adjuvant therapy. Meta-analysis showed … [1]: {openurl}?query=rft.jtitle%253DCochrane%2BDatabase%2BSyst%2BRev%26rft.volume%253D4%26rft.spage%253DCD001877%26rft_id%253Dinfo%253Apmid%252F17054145%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=17054145&link_type=MED&atom=%2Febnurs%2F10%2F3%2F80.atom [3]: /embed/inline-graphic-1.gif [4]: /embed/inline-graphic-2.gif [5]: /embed/inline-graphic-3.gif
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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 | 0.014 | 0.093 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".