What Influences Diagnostic Delay in Low-Income Women with Breast Cancer?
Bibliographic record
Abstract
BACKGROUND: Delayed diagnosis of breast cancer (BC) may contribute to adverse outcomes, such as reduced survival. The purpose of this study was to identify correlates of elapsed time between recognition of breast abnormalities and receipt of definitive diagnosis of BC among low-income women. METHODS: Data were obtained from a cross-sectional study among a statewide sample of 921 low-income women with a new diagnosis of BC. Patients were grouped by whether their breast abnormalities were self-detected or healthcare system detected. Multivariate logistic regression analyses were used to examine associations between diagnostic delay and patient characteristics, patient communication, and system characteristics. RESULTS: The self-detected group experienced much greater delay than the system-detected group (median intervals 80.5 vs. 31.5 days). African Americans had the longest intervals between symptom detection and diagnostic resolution; median delays in the self-detected and system-detected subgroups were 115 and 70 days, respectively, compared to 64 and 22 days for Caucasians. In multivariate analyses, African Americans had considerably greater odds of >60-day delay than Caucasians in both the self-detected (odds ratio [OR] 3.51) and system-detected (OR 5.36) groups. Greater perceived self-efficacy in interacting with healthcare providers was significantly associated with shorter delay among the self-detected group (OR 0.86). CONCLUSIONS: Disparities in timely BC diagnosis between African Americans and Caucasians were pronounced in this uniformly low-income population of women. Women with self-detected abnormalities had markedly greater delays than those with healthcare system-detected abnormalities. Among this vulnerable group, increasing self-efficacy in interacting with healthcare providers may reduce diagnostic delays.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".