Claims data linked to hospital registry data enhance evaluation of the quality of care of breast cancer
Bibliographic record
Abstract
BACKGROUND: Complete treatment data is central to evaluation and improvement of cancer care quality. Cancer registries vary in completeness of radiation (RT), chemotherapy (CT), and hormone therapy (HT) data. Administrative claims from health payers may supplement these registries. This study assesses the ability to link private payer claims to the National Cancer Data Base (NCDB) and the extent of additional treatment data identified in claims. METHODS: Claims for patients with breast cancer surgery from one payer in Western New York (WNY) were matched with NCDB for participating hospitals for 2001-2003 using available identifiers (reporting hospital, gender, birth date, ZIP code). Claims were analyzed for breast and axillary surgery, RT, CT, and HT, and compared with treatment recorded in the NCDB. RESULTS: Four hundred seventy women had claims for breast cancer surgery and 439 (91%) matched to the NCDB. Seventeen had duplicate/incomplete records. Non-matches included cases with surgery for cancer recurrence. Among 422 evaluable cases, stage was 0: 9%; I: 49%; II: 33%; III: 7%; and IV: 2%. Claims and registry were highly concordant for surgery. Registry identified RT, CT, and HT in 38%, 47%, 18%, respectively, of treatment reported in claims. Claims also provided information on drugs used and treatment duration. CONCLUSIONS: The NCDB can be matched with private payer claims using available identifiers. Registry data in this convenience sample of hospitals did not include a substantial fraction of outpatient data identified by claims. Private payer claims may help enhance the completeness of NCDB treatment information.
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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 | 0.051 | 0.191 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| 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".