High triple‐negative breast cancer prevalence and aggressive prognostic factors in Barbadian women with breast cancer
Bibliographic record
Abstract
BACKGROUND: Women of African ancestry (WAA) are disproportionately affected by triple-negative breast cancer (TNBC), which remains one of the most clinically challenging breast cancer (BCa) subtypes. This study investigated the prevalence of TNBC and epidemiological trends for BCa in Barbados, a Caribbean island with a high percentage of African ancestry. METHODS: Pathology reports for all BCa cases between 2007 and 2016 were collected from the sole hospital in Barbados and reviewed. The clinicopathological data collected included age, tumor grade, lymph node status, and hormone receptor status as determined by immunohistochemistry. BCa data for non-Hispanic white (NHW) and non-Hispanic black (NHB) American populations were accessed from the Surveillance, Epidemiology, and End Results database. RESULTS: There were 1997 BCa cases in Barbados between 2007 and 2016 for an estimated incidence rate of 135.1 per 100,000 women in Barbados (standardized to the US population, where the standardized incidence rates for NHBs and NHWs were 141.4 and 152.6 per 100,000, respectively). Age-specific incidence rates in Barbados for this period were consistently higher in younger age groups (15-59 years) in comparison with NHWs and NHBs. Between 2010 and 2016 in Barbados, a TNBC prevalence of 25% was observed, whereas TNBC prevalences of 21% and 10% were observed in NHBs and NHWs, respectively. CONCLUSIONS: The BCa incidence was higher in younger Barbadian women than NHWs and NHBs, and the TNBC prevalence was ~2.5 times higher than the prevalence in NHWs. This hints at a possible genetic predisposition and other socioeconomic factors that could explain the high TNBC prevalence and aggressive clinical course in WAA globally.
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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 | 0.000 | 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 | 0.000 | 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".