Bibliographic record
Abstract
NAACCR would like to thank its members, the population-based central cancer registries throughout North America, for submission of their cancer incidence data to NAACCR. Through voluntary participation in the annual NAACCR Call-for-Data, incidence data from up to 78 cancer registries in the United States and Canada are evaluated each year for timeliness, accuracy, and completeness. The number of registries meeting the NAACCR data quality standards for inclusion in an aggregated database grows each year. NAACCR staff, NAACCR committees, and individual researchers use the aggregated database, called CINA Deluxe, to conduct surveillance and epidemiologic research and to continually evaluate the comparability and quality of the data. The research and publications generated from these analyses reflect the efforts of many NAACCR volunteers representing a broad spectrum of cancer registries and cancer surveillance organizations in the United States and Canada. We appreciate the dedication of these professionals to use this valuable national resource to study the cancer burden in North America and to enhance our understanding of risk, etiology, and diversity in cancer occurrence. Further information can be obtained by contacting the NAACCR Executive
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.364 | 0.244 |
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".