Stage of breast cancer at diagnosis among low‐income women with access to mammography
Bibliographic record
Abstract
BACKGROUND: This study assessed the relationship between area-level poverty and stage of breast cancer at diagnosis among low-income women when screening mammography was available at no cost. METHODS: The authors identified women diagnosed with breast cancer from 1999 to 2005 through the Massachusetts Cancer Registry, and compared the odds of advanced stage disease for women with low incomes (n=546) for whom screening mammography and diagnostic services were available at no cost through the Massachusetts Breast and Cervical Cancer Early Detection Program, relative to a nonparticipating comparison group (n=1287) residing in the same neighborhoods with similar distribution of age, race, and ethnicity as Massachusetts Breast and Cervical Cancer Early Detection Program participants. Among Massachusetts Breast and Cervical Cancer Early Detection Program participants, the odds of advanced stage disease were estimated by mammography use. RESULTS: Although screening mammography was available at no cost, only 36% of program participants diagnosed with breast cancer used screening mammography. Stage of breast cancer at diagnosis was not associated with area-level poverty among Massachusetts Breast and Cervical Cancer Early Detection Program participants. For the comparison group, advanced stage disease was more likely for residents in high-poverty areas, relative to low-poverty areas (49% vs 37%, P<.01). The adjusted odds of advanced stage disease at diagnosis was greater for women aged 41 to 49 years, compared with those aged 50 to 64 years (P=.01). CONCLUSIONS: Programs that ensure breast cancer screening and diagnostic services are available at no cost to low-income women can mitigate the adverse effect of area-level poverty on stage of breast cancer. However, such programs require effective strategies to encourage use of screening mammography to promote diagnosis at an earlier stage.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".