Using personal health insurance numbers to link the Canadian Cancer Registry and the Discharge Abstract Database.
Bibliographic record
Abstract
BACKGROUND: Linking cancer registry and administrative data can reveal health care use patterns among cancer patients. The Canadian Cancer Registry (CCR) contains personal health insurance numbers (HINs) that facilitate linkage to hospitalization information in the Discharge Abstract Database (DAD). DATA AND METHODS: Valid HINs, captured in the CCR or obtained through probabilistic linkages to provincial health insurance registries, were used to deterministically link prostate, female breast, colorectal and lung cancers diagnosed from 2005 through 2008 with the DAD for fiscal years 2004/2005 to 2010/2011. RESULTS: At least 98% of tumours diagnosed from 2005 through 2008 had valid HINs in the CCR or obtained through probabilistic linkages. For provinces submitting day surgeries to the DAD, linkage rates to at least one DAD record were higher for female breast (95.6% to 98.1%), colorectal (96.9% to 98.7%) and lung cancers (92.8% to 96.3%) than for prostate cancers (77.2% to 91.6%). Among linked records, agreement was high for sex (99% or more) and complete date of birth (97% or more); the likelihood of a consistent diagnosis in the CCR and on at least one linked DAD record was higher for female breast (86.8% to 97.2%), colorectal (94.6% to 97.7%) and lung cancers (90.3% to 95.5%) than for prostate cancers (77.4% to 87.8%). INTERPRETATION: Deterministically linking the CCR and DAD using personal HINs is a feasible and valid approach to obtaining hospitalization information about cancer patients.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".