Validation in Alberta of an Administrative Data Algorithm to Identify Cancer Recurrence
Bibliographic record
Abstract
Background: Readily available population-based data about cancer recurrence would improve surveillance and research for women of reproductive age. Methods: We randomly selected 200 women from the Alberta Cancer Registry who had received a cancer diagnosis and who ever had a pregnancy between 2003 and 2012. Administrative data were obtained and linked. Several definitions of recurrence were assessed using various minimum lengths of time between the initial diagnosis date and subsequent diagnoses or treatments, or both. Chart review was used as a "gold standard" definition of recurrence. Results: Chart review identified recurrences in 26 women. The definition that best captured "recurrence" was 2 or more cancer diagnosis codes 10 or more months from the diagnosis date [sensitivity: 80.8%; 95% confidence interval (ci): 60.7% to 93.5%; specificity: 81.0%; 95% ci: 74.4% to 86.6%; positive predictive value: 38.9%; 95% ci: 25.9% to 53.1%; negative predictive value: 96.6%; 95% ci: 92.2% to 98.9%; kappa = 0.42; 95% ci: 0.28 to 0.57]. Conclusions: Recurrence in reproductive-aged women can be captured with moderate validity using administrative data, but should be interpreted with caution.
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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 | 0.019 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".