Characteristics of State Policies Impact Health Care Delivery
Bibliographic record
Abstract
BACKGROUND: Increased breast tissue density may mask cancer and thus decrease the diagnostic sensitivity of mammography. A patient group advocacy led to the implementation of laws to increase the awareness of breast tissue density and to improve access to supplemental imaging in many states. Given limited evidence about best practices, variation exists in several characteristics of adopted policies. OBJECTIVE: To identify which characteristics of state-level policies with regard to dense breast tissue were associated with increased use of downstream breast ultrasound. RESEARCH DESIGN: This was a retrospective series of monthly cross-sections of screening mammography procedures before and after implementation of laws. SUBJECTS: A sample of 13,481,554 screening mammography procedures extracted from the MarketScan Research database performed between 2007 and 2014 on privately insured women aged 40-64 years that resided in a state that had implemented relevant legislation during that period. MEASURES: The outcome was an indicator of whether breast ultrasound imaging followed a screening mammography procedure within 30 days. The main independent variables were policy characteristics indicators. RESULTS: Notification of patients about issues surrounding increased breast density was associated with increased follow-up by ultrasound by 1.02 percentage points (P=0.016). Some policy characteristics such as the explicit suggestion of supplemental imaging or mandated coverage of supplemental imaging by health insurance augmented that effect. Other policy characteristics moderated the effect. CONCLUSIONS: The heterogeneous effect of state legislation with regard to dense breast tissue on screening mammography follow-up by ultrasound may be explained by specific and unique characteristics of the approaches taken by a variety of states.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".