Validity of Administrative Databases in Comparison to Medical Charts for Breast Cancer Treatment Data
Bibliographic record
Abstract
Objective. Medical chart abstraction is the gold standard for collecting breast cancer treatment data for monitoring and research. A less costly alternative is the use of administrative databases. This study will evaluate administrative data in comparison to medical charts for breast cancer treatment information.Study Design and Setting. A retrospective cohort design identified 2,401 women in the Ontario Breast Screening Program diagnosed with invasive breast cancer from 2006 to 2009. Treatment data were obtained from the Activity Level Reporting and Canadian Institute of Health Information databases. Medical charts were abstracted at cancer centres. Sensitivity, specificity, positive and negative predictive value, and kappa were calculated for receipt and type of treatment, and agreement was assessed for dates. Logistic regression evaluated factors influencing agreement.Results. Sensitivity and specificity for receipt of radiotherapy (92.0%, 99.3%), chemotherapy (77.7%, 99.2%), and surgery (95.8%, 100%) were high but decreased slightly for specific radiotherapy anatomic locations, chemotherapy protocols, and surgeries. Agreement increased by radiotherapy year (trend test, p<0.0001 ). Stage II/III compared to stage I cancer decreased odds of agreement for chemotherapy (OR = 0.66, 95% CI: 0.48–0.91) and increased agreement for partial mastectomy (OR = 3.36, 95% CI: 2.27–4.99). Exact agreement in treatment dates varied from 83.0% to 96.5%.Conclusion. Administrative data can be accurately utilized for future breast cancer treatment studies.
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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 | 0.138 | 0.431 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".