Completeness of a newly implemented general cancer registry in northern France: Application of a three-source capture-recapture method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Completeness, timeliness and accuracy are important qualities for registries. The objective was to estimate the completeness of the first two years of full registration (2008/2009) of a new population-based general cancer registry, at the time of national data centralisation. METHODS: Records followed international standards. Numbers of cases missed were estimated from a three-source (pathology labs, healthcare centres, health insurance services) capture-recapture method, using log-linear models for each gender. Age and place of residence were considered as potential variables of heterogeneous catchability. RESULTS: When data were centralized (2011/2012), 4446 cases in men and 3642 in women were recorded for 2008/2009 in the Registry. Overall completeness was estimated at 95.7% (95% CI: 94.3-97.2) for cases in men and 94.8% (95% CI: 92.6-97.0) in women. Completeness appeared higher for younger than for older subjects, with a significant difference of 4.1% (95% CI: 1.4-6.7) for men younger than 65 compared with their older counterparts. Estimates were collated with the number of cases registered in 2014 for the years 2008/2009 (4566 cases for men/3755 for women), when additional structures had notified cases retrospectively to the Registry. These numbers were consistent with the stratified capture-recapture estimates. CONCLUSION: This method appeared useful to estimate the completeness quantitatively. Despite a rather good completeness for the new Registry, the search for cases among older subjects must be improved.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it