Ten-year survival in early-stage breast cancer patients in a comprehensive breast cancer care program in India
Bibliographic record
Abstract
Introduction: Breast cancer accounted for 21.9% of all cancer deaths among women in India in 2020. Fifty seven percent of the breast cancers in India are detected at advanced stages. The lack of adequate resources for diagnosis and treatment adds to the delay and reduces survival. The clinical stage at diagnosis is the most important prognostic factor. Increased cancer awareness, early diagnosis, and affordable and accessible treatment facilities have been recommended for clinical downstaging and improved survival in low- and middle-income countries including India. We implemented a comprehensive breast care program based on these recommendations. This study explores the long-term survival outcomes of patients diagnosed with early-stage breast cancer (EBC) in an early detection program within a universal health coverage (UHC) scheme. Methods: This is a cohort study of women diagnosed with early-stage breast cancer under the UHC scheme between 2008 and 2018. The follow-up was done through electronic medical records, in-person clinic visits, and telephone calls. The primary outcomes were 5- and 10-year overall survival and disease-free survival. Results: A total of 185 patients who presented with EBC were recruited among 254 incident breast cancer cases throughout the study period (72.8%). The average overall survival was 123 months. Five-year overall and disease-free survival were 85.2 and 84.6%, respectively. Ten-year overall and disease-free survival were 79.0 and 76.2%, respectively. Discussion: This study underscores the importance of early detection in breast cancer. It also demonstrates that 5- and 10-year survival rates better than those reported in Indian cancer registries are achievable through comprehensive cancer care and UHC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".