Using data linkage to report surgical treatment of breast cancer in Canada.
Bibliographic record
Abstract
BACKGROUND: National population information about the surgical treatment rate for primary cancers, including breast cancer, has remained a significant data gap in Canada. This gap has implications for cancer care planning and evaluating health system performance. New linkages between the Canadian Cancer Registry and hospital discharge records were conducted by Statistics Canada in 2016. Using already existing, routinely collected health administrative data, these linkages allow viable reporting of surgical cancer treatment for the first time for all provinces and territories (except Quebec). DATA AND METHODS: Hospital record information about type and date of surgical treatment of tumours was provided by information from linked data. These linked data reported 50,740 incident primary malignant breast tumours diagnosed between January 1, 2010, and December 31, 2012, among females aged 19 years or older. The unadjusted treatment rate for primary surgical intervention within one year was calculated as the proportion of total tumours that were linkable to hospital records. RESULTS: For three combined years (2010, 2011 and 2012), 88.3% (N=44,780) of patients overall received at least one surgical treatment. Variations to the surgical rate occurred across jurisdictions, with the highest rate at 91-92% for Prince Edward Island, Newfoundland and Labrador, British Columbia and New Brunswick. Generally, there was an inverse gradient between surgical treatment rate and tumour stage. DISCUSSION: The surgical treatment rate of new primary breast cancers varied across provinces and territories from 2010 to 2012. New linked data could be used to further identify geographic and demographic inequities in terms of receiving surgical cancer treatment and contribute to the evaluation of cancer system performance and outcomes.
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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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.031 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".