Completeness of a newly implemented general cancer registry in northern France: Application of a three-source capture-recapture method
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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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 | 0.048 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".