Does the extent of therapy differ between breast cancers detected by screening mammogram and non-screening methods?
Bibliographic record
Abstract
1544 Background: There is ongoing debate about the role of screening mammography and its impact on overall survival in breast cancer. We hypothesized that women with screen-detected breast cancers (SDBC) receive less surgery, regional radiotherapy (RRT), and chemotherapy (CH) than women with non-screen-detected breast cancers (NSDBC). Less therapy equates to less personal and societal burden, including less time away from work, fewer side effects, lower health care and disability costs, and reduced psychosocial distress. These may be adequate justification for screening programs even in the absence of an overall survival benefit. Methods: Women aged 40-79 years with stage 0-III breast cancers diagnosed between 2007-2012 and referred to the British Columbia Cancer Agency were identified using the Breast Cancer Outcomes Unit database. Clinical and tumor characteristics and type/extent of treatment were extracted. Linkage with the Screening Mammography Program of British Columbia segregated cases into SDBCs and NSDBCs. Interval breast cancers arising in regularly screened women (minimum 2-year interval) were excluded. Results: We identified 12,393 women; 7807 with SDBC and 4586 with NSDBC. Compared with NSDBCs, SDBCs were lower stage, less often treated with mastectomy and CH, and occurred in slightly older women (Table 1). SDBC received more radiation than NSDBC. Conclusions: Women with NSDBC are more likely to present with higher stage breast cancer. Rates of mastectomy and CH were 20% higher in NSDBC whereas SDBC had a modest 5% higher rate of RRT. These findings suggest that screening mammography decreases the extent of local and systemic treatment for breast cancer. [Table: see text]
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".