Carriage of HLA-DRB1*11 and 1*12 alleles and risk factors in patients with breast cancer in Burkina Faso
Bibliographic record
Abstract
Abstract Several factors contribute to the development of breast cancer, including the immune system. This study is aimed to characterize the carriage of human leukocyte antigen (HLA)-DRB1*11 and 1*12 alleles in patients with breast cancer. This case-control study consisted of 96 histologically diagnosed breast cancer cases and 102 controls (cases without breast abnormalities). A multiplex polymerase chain reaction (PCR) was used to characterize the carriage of HLA-DRB1*11 and 1*12 alleles. The HLA-DRB1*11 allele was present in 26.59% of cases and 22.55% of controls. The HLA-DRB1*12 allele was present in 56.63% of cases and 55.88% of controls. This study found no direct association between the carriage of the HLA-DRB1*11 and HLA-DRB1*12 alleles and the occurrence of breast cancer. In addition, the deletion of the HLA-DRB1*11 allele is associated (beneficial effect) with obesity/overweight (OR = 0.13; 95% CI [0.01–1.14]; and p = 0.03) which is a risk for breast cancer. No direct association was found between the carriage of HLA-DRB1*11 and 1*12 alleles and breast cancer risk. However, further investigation of other HLA alleles involved in the occurrence of breast cancer may provide more information.
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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 | 0.002 |
| 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 | 0.000 |
| 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".