Breast Cancer and HIV in Sub-Saharan Africa: A Complex Relationship
Bibliographic record
Abstract
INTRODUCTION: The number and lifespan of individuals living with HIV have increased significantly with the scale-up of antiretroviral therapy. Furthermore, the incidence of breast cancer in women with HIV is growing, especially in sub-Saharan Africa (SSA). However, the association between HIV infection and breast cancer is not well understood. METHODS: A literature search was performed to identify articles published in journals pertaining to breast cancer and HIV, with an emphasis on SSA. Selected US-based studies were also identified for comparison. RESULTS: Among the 56 studies reviewed, the largest study examined 314 patients with breast cancer and HIV in the United States. There is no consensus on whether HIV infection acts as a pro-oncogenic or antioncogenic factor in breast cancer, and it may have no relation to breast cancer. A higher incidence of breast cancer is reported in high-income countries than in SSA, although breast cancer in SSA presents at a younger age and at a more advanced stage. Some studies show that patients with breast cancer and HIV experience worse chemotherapy toxicity than do patients without HIV. Data on treatment outcomes are limited. The largest study showed worse treatment outcomes in patients with HIV, compared with their counterparts without HIV. CONCLUSION: HIV infection has not been associated with different clinical presentation of breast cancer. However, some evidence suggests that concurrent diagnosis of HIV with breast cancer is associated with increased therapy-related toxicity and worse outcomes. Systematic prospective studies are needed to establish whether there is a specific association between breast cancer and HIV.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".