Cancer incidence among HIV‐positive women in British Columbia, Canada: Heightened risk of virus‐related malignancies
Bibliographic record
Abstract
OBJECTIVES: We used population-based data to identify incident cancer cases and correlates of cancer among women living with HIV/AIDS in British Columbia (BC), Canada between 1994 and 2008. METHODS: Data were obtained from a retrospective population-based cohort created from linkage of two province-wide databases: (1) the database of the BC Cancer Agency, a province-wide population-based cancer registry, and (2) a database managed by the BC Centre for Excellence in HIV/AIDS, which contains data on all persons treated with antiretroviral therapy in BC. This analysis included women (≥ 19 years old) living with HIV in BC, Canada. Incident cancer diagnoses that occurred after highly active antiretroviral therapy (HAART) initiation were included. We obtained a general population comparison of cancer incidence among women from the BC Cancer Agency. Bivariate analysis (Pearson χ(2) , Fisher's exact or Wilcoxon rank-sum test) compared women with and without incident cancer across relevant clinical and sociodemographic variables. Standardized incidence ratios (SIRs) were calculated for selected cancers compared with the general population sample. RESULTS: We identified 2211 women with 12 529 person-years (PY) of follow-up who were at risk of developing cancer after HAART initiation. A total of 77 incident cancers (615/100 000 PY) were identified between 1994 and 2008. HIV-positive women with cancer, in comparison to the general population sample, were more likely to be diagnosed with invasive cervical cancer, Hodgkin's lymphoma, non-Hodgkin's lymphoma and Kaposi's sarcoma and less likely to be diagnosed with cancers of the digestive system. CONCLUSIONS: This study observed elevated rates of cancer among HIV-positive women compared to a general population sample. HIV-positive women may have an increased risk for cancers of viral-related pathogenesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".