Time Trends in Cervical 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 cervical cancer screening rates 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), Hungary, Belgium and Netherlands (Europe). The cervical cancer screening rates reported by OECD were derived from ‘programme data’ or ‘survey data’ primarily for women aged 20–69 years, though the age range was somewhat different in some countries (30–69 in Korea, 18–69 in Canada, 30–60 in Finland, 25–69 in Norway, 20–64 in the UK, 25–65 in Hungary, 25–64 in Belgium and 30–60 in Netherlands).
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".