Breast cancer in Trinidad and Tobago
Bibliographic record
Abstract
Background: Breast Carcinoma (BCa) is the leading cause of cancer among females in Trinidad and Tobago (TnT). This twin-island has a diversified population of 1.3 million individuals that display and are exposed to a variety of lifestyle choices that have been linked to the development of BCa. Therefore, this study aimed to identify the risk factors that influence the development of BCa, analyze the common histopathological details, and categorize BCa based on receptor study. Methods: Cancer information for 120 BCa cases at Eric Williams Medical Sciences Complex from 2012 to 2019 was retrieved, analyzed, and statistically estimated. The clinical details were categorized based on data tabulations, and histological assessment was performed to identify specific features. The receptor analysis was classified based on estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor-2 (HER-2neu) staining intensity. A descriptive data analysis and comparison were statistically evaluated in all these cases. Results: Epidemiological factors influencing the development of BCa were age with a peak of 56-65 years 27.5% (n = 33), ethnicity predominated in Indo-Trinidadians 48.33% (n = 58), and marital status primarily in unmarried/single/widowed patients 55% (n = 66). Infiltrating ductal carcinoma was the principal histopathological type 91.66% (n = 110). Receptor analysis revealed ER/PR + HER-2neu as the most common type 40% (n = 18) for therapeutic surveillance. Conclusion: This study highlights various epidemiological factors that influence the development of BCa among females in TnT. Histopathological analysis and receptor studies would provide a useful link between the tumor behavior and its prognosis.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".