Socio-Demographic Inequalities in Diagnostic Delays of Breast Cancer: A Multistage Time-to-Diagnosis Analysis
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Introduction: Breast cancer remains a leading cause of cancer morbidity and mortality among women, and timely diagnosis is critical for improving outcomes. Organized screening programs strive to function efficiently, with minimal delays; however, evidence indicates that longer waiting times may be present at different stages of the diagnostic process. Few studies have evaluated how socio-demographic, reproductive, lifestyle, and clinical characteristics may influence diagnostic timeliness in a regional screening context. MATERIALS AND METHODS: We retrospectively analyzed data from 240 women who underwent breast biopsy following abnormal screening assessment, out of 24,000 patients enrolled in a regional breast cancer screening program conducted in Northeastern and Southeastern Romania. Diagnostic timeliness was observed across three consecutive intervals of the screening pathway: mammography to biopsy (T1), biopsy to histopathological confirmation (T2), and cumulative presentation-to-diagnosis time (T3). Baseline population characteristics were described, subgroup comparisons performed, and multivariable regression models applied to identify independent predictors of diagnostic delay and to explore interaction effects at different stages of the screening process. RESULTS: = 0.003). Social vulnerability further contributed to prolonged T1 and T3 intervals, while lifestyle, reproductive, and anthropometric factors showed only minor or inconsistent associations. Interaction analyses revealed that delays linked to rural residence were most pronounced among younger women, an age group at higher risk for aggressive subtypes such as triple-negative breast cancer. CONCLUSIONS: In our findings, regional background and social vulnerability outweighed individual risk factors in shaping total diagnostic time. These results support the careful monitoring of interval-specific performance to strengthen equitable access to biopsy among vulnerable populations, where the effectiveness of early breast cancer detection is often challenged.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".