An examination of the NAACCR method of assessing completeness of case ascertainment using the Canadian Cancer Registry.
Bibliographic record
Abstract
BACKGROUND: Despite use of the North American Association of Central Cancer Registries' indicator for assessing completeness of case ascertainment in population-based cancer registries, little has been published about its methodology, usefulness and accuracy in Canada. DATA AND METHODS: Canadian cancer incidence, cancer mortality, and population census data were used to quantify case completeness in 2007. Two indicators (I₁ and I₂) that expressed the observed age-standardized incidence rate relative to the expected rate were calculated. The assumption of stable age-standardized sex- and cancer-site-specific incidence-to-mortality rate ratios across regions was assessed. Associations between I₁, I₂ and simpler indicators of completeness were examined. RESULTS: The assumption of stable age-standardized sex- and cancer-site-specific incidence-to-mortality rate ratios across regions was not consistently supported—substantial regional differences emerged. I₁ was strongly correlated with I₂ (r=0.93, n=315, p<0.0001), and both were most strongly and consistently associated with the age-standardized incidence-to-mortality rate ratio. The frequency of undercoverage did not increase consistently with expected case-finding difficulty. INTERPRETATION: The age-standardized incidence-to-mortality rate ratio may provide a less complicated method of identifying undercoverage.
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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 | 0.191 | 0.423 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".