Intercontinental Comparison of Immunohistochemical Subtypes Among Individuals With Breast Cancer in South-East Asia and South America: A Scoping Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
Background: Breast cancer (BC) remains a significant global concern, particularly among developing countries in South-East Asia (SEA) and South America (SA). The socioeconomic burdens of oncologic care in those countries were often originated from limited accessibility on attainable therapeutic options and reliability on identifying essential information of cancer cells, i.e., immunohistochemical (IHC) subtyping to determine suitable approaches. The triple-negative breast cancer (TNBC) is among the most aggressive category in breast malignancy, therefore, requiring more specific molecular pathway blocking to exhaust the cells. However, large-scale epidemiological investigation on its rate among BC remains unavailable to date. This study aimed to describe the prevalence of TNBC in the SEA and SA continents since it may guide the future direction of oncologic research and trials. Methods: This review focuses on observational studies from the SEA and SA continents from the last decade. Each study represents its country or cities, period of observation, population size, and the TNBC-BC rate as the main outcomes. Therefore, we may also limit the reporting bias originated from same-patient data on the specific occasions. The analysis will be derived to SEA-SA comparison, plus SEA/SA-specific session as processed in Comprehensive Meta-Analysis (CMA) version 3.0. The statistical analysis will be performed in random effects model (REM) within 95% confidence interval (CI). Results: From 46 studies included in the final analysis with a total enlisted population of 34,346 unique individuals with BC, the TNBC rate was higher in the SEA compared to the SA region (19.3% vs. 15.7%; P < 0.05 in 95% CI), with the highest prevalence observed in Vietnam (22.4%) and Peru (17.8%), if it was restricted on countries with two or more studies. Interestingly, both Laos and Argentina possessed significant differences compared to other countries within their respective continents, with the highest and lowest TNBC rates (P < 0.05). Conclusions: The IHC characteristics in SEA differ from those in the SA continent as mainly represented by TNBC prevalence, possibly shaping the course of future trials in the respective region based on IHC expressivity status.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".