Improving the usability and utilization of cancer registry data : the need to identify a core data set
Bibliographic record
Abstract
Cancer registries in the US and Canada have a long history of data standards and data collection that have developed from a minimal dataset to the standard dataset that is used now. Central Cancer Registries (CCRs) are good resources for cancer data, but are often underutilized. CCRs are recognized for high quality data standards by the Centers for Disease Control and Prevention (CDC) National Program of Cancer Registries (NPCR) or the National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program and receive certification from the North American Association of Central Registries (NAACCR). Each year, there are many changes to the data that are collected in the cancer registry field. Standards, requirements, and medical knowledge change frequently. The changes in the data collection process cause interference and decrease in quality of data fields, but also delays in the timely collection of cancer registry data. The objective of this study is to identify what essentially needs to be collected and what can be collected optionally in a cancer registry. The goal is a robust dataset that can be used for other disease registries, cancer data surveillance, public health, and research. CCRs and Cancer Centers (CR) were surveyed to identify and describe the data items that are collected and needed to achieve a dataset that can serve cancer surveillance and research. The surveys were analyzed to identify overlaps of common and special interests, as well as barriers. The results showed that cancer registries have data available, but need to look at the timely release of a core dataset for use in cancer surveillance and research. The surveys also evaluated the barriers to data use from cancer registries and barriers for data use of collected datasets to identify the initial data request process. Data in the cancer registry are in a format that can easily be adopted by public health, surveillance, and research. The requesting process needs to be accessible, understandable, and streamlined to enable successful use of the data.
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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 | 0.489 | 0.758 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.015 | 0.020 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.008 | 0.016 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".