Collection and Reporting of National Cancer Stage at Diagnosis Data in Australia (STaR Project)
Bibliographic record
Abstract
Background: Stage at diagnosis is an important prognostic factor for cancer, providing contextual information for interpreting population health indicators such as mortality from cancer and cancer survival. Australian population-based cancer registries (PBCRs) routinely collect information on cancer incidence and mortality. The need for high quality, comprehensive national data on stage at diagnosis to supplement these data are widely recognized in Australia. The collection and dissemination of quality national stage data will enhance the: • ability to better monitor cancer outcomes, inform cancer control policy; • understand variations across different populations; and • identify where further research and targeted strategies may be required to improve cancer outcomes. Linking data on cancer stage at diagnosis with other administrative cancer data will also allow for a better understanding of the relationship between stage at diagnosis, treatments received, patterns of cancer recurrence, and survival outcomes. Aim: To strengthen national data capacity by collecting and reporting cancer stage at diagnosis for Cancer Australia's Stage, Treatment and Recurrence (STaR) project. Methods: Working with state and territory population-based cancer registries (PBCRs) and the Australian Pediatric Cancer Registry, Cancer Australia supported the development and testing of Business Rules for the collection of national cancer stage at diagnosis for: • The top 5 incident cancers based on the Tumor, Node, and Metastasis (TNM) staging system. These rules were endorsed by the Australasian Association of Cancer Registries (AACR) as a national standard in May 2016; and • Childhood cancers, with a separate set of Business Rules for 16 childhood cancer types based on the Toronto Pediatric Cancer Stage Guidelines. These rules were supported by the AACR as a national standard. Results: Using the AACR-endorsed Business Rules, comprehensive national cancer stage at diagnosis data for the top 5 incident cancers (for 2011) have been collected in Australia for the first time. Over 90% of incidence cases were able to be assigned a value for registry-derived (RD) stage at diagnosis for melanoma (97%), prostate (97%), and female breast (94%) cancers. Lower staging completeness was found for colorectal cancers (88%), and for lung cancers (72%). Business Rules for the collection of stage at diagnosis data for pediatric cancers have also been developed; 93% of sample cases diagnosed in the period 2006-2010 were able to be staged, ranging from 84% for nonrhabdomyosarcoma to 100% for hepatoblastoma. Conclusion: The Business Rules enabled the uniform collection of cancer stage at diagnosis data for the first time in Australia. The collection of these data will allow for the linkage of stage at diagnosis to other sources of information, including patterns of treatments applied, and enable reporting of survival and recurrence outcomes by stage.
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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 | 0.049 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".