Rhode Island (RI) Women's Breast Cancer Mammography Use Prior to and After Cancer Diagnosis: Linkage of RI Cancer Registry Data With RI All-Payer Claims Database
Bibliographic record
Abstract
OBJECTIVE: A limitation of the central cancer registries to examine associations between mammography use and cancer diagnosis is their lack of cancer screening history. To fill this measurement gap, Rhode Island Cancer Registry (RICR) breast cancer (BC) records were linked to Rhode Island-all-payer claims database (RI-APCD) to study Rhode Island (RI) women's regular mammography use and identify its predictors. METHODS: From the linked 2011-2019 data, we identified 4 study cohorts: (1) women who ever received mammography by Women's Cancer Screening Program (WCSP) and were diagnosed with BC ("WCSP-BC" cohort: n = 149), (2) women diagnosed with BC outside of WCSP (BC-control cohort: n = 4304), (3) women with a history of mammography use at WCSP but no BC diagnosis (n = 6513), and (4) general RI women with no BC diagnosis (n = 15 121). Logistic regressions were conducted to identify predictors of regular mammography use. RESULTS: The linkage for RI-APCD and RICR for our study had a high matching rate of 82%. Mammography use prior to BC diagnosis was not different between the WCSP-BC cohort and the BC-control cohort (58% vs 57%). Women in the BC-control cohort who had mammography in 2 years prior to their cancer diagnosis were more likely of being diagnosed at an early-stage disease. Among BC-control group, women with no anxiety/depression or with no preventive examinations were less likely of regular mammography use. Among women with no BC, a lower proportion of women with a history of screening at WCSP had regular mammography use, compared with the general RI women (38% vs 66%). CONCLUSION: RI-APCD data linkage with RICR provides excellent opportunities to examine regular mammography use among RI women and compare their outcomes to the general women population in the state. We identified opportunities for improving their mammography use. A measurement gap in the central cancer registries can be effectively reduced by utilizing statewide claims database.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".