Time Trends in Breast Cancer Screening Rates in the OECD Countries
Bibliographic record
Abstract
Cancer screening rates were reported by the Organization for Economic Co-operation and Development (OECD) in Health Data 2010, which presents the existing set of quality of care indicators considered suitable for international comparison. We used mammography screening rates of breast cancer from 12 OECD countries. The screening rates were reported during the period 2000–09. The selected OECD countries, which had sufficient information, were Japan and the Republic of Korea (Asia); the United States of America (USA) and Canada (America); Australia and New Zealand (Oceania); Finland, Norway, the United Kingdom (UK), the Czech Republic, Belgium and Netherlands (Europe). The mammography screening rates reported by OECD were based on ‘programme data’ or ‘survey data’ for women aged 50–69 years. The ‘programme data’, which has national coverage, were used for the all European and Oceanian countries studied; the ‘survey data’ based on a national representative sample, were used for the Asian countries and the USA and Canada. The screening rates were based on women aged 50–69 years who have completed the survey on mammography (survey data) or were eligible for organized screening programme (programme data) and reported having received a bilateral mammography according to the specific screening frequency recommended in each country.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".