Cervical cancer risk in women living with HIV across four continents: A multicohort study
Bibliographic record
Abstract
We compared invasive cervical cancer (ICC) incidence rates in Europe, South Africa, Latin and North America among women living with HIV who initiated antiretroviral therapy (ART) between 1996 and 2014. We analyzed cohort data from the International Epidemiology Databases to Evaluate AIDS (IeDEA) and the Collaboration of Observational HIV Epidemiological Research in Europe (COHERE) in EuroCoord. We used flexible parametric survival models to determine regional ICC rates and risk factors for incident ICC. We included 64,231 women from 45 countries. During 320,141 person-years (pys), 356 incident ICC cases were diagnosed (Europe 164, South Africa 156, North America 19 and Latin America 17). Raw ICC incidence rates per 100,000 pys were 447 in South Africa (95% confidence interval [CI]: 382-523), 136 in Latin America (95% CI: 85-219), 76 in North America (95% CI: 48-119) and 66 in Europe (95% CI: 57-77). Compared to European women ICC rates at 5 years after ART initiation were more than double in Latin America (adjusted hazard ratio [aHR]: 2.43, 95% CI: 1.27-4.68) and 11 times higher in South Africa (aHR: 10.66, 95% CI: 6.73-16.88), but similar in North America (aHR: 0.79, 95% CI: 0.37-1.71). Overall, ICC rates increased with age (>50 years vs. 16-30 years, aHR: 1.57, 95% CI: 1.03-2.40) and lower CD4 cell counts at ART initiation (per 100 cell/μl decrease, aHR: 1.25, 95% CI: 1.15-1.36). Improving access to early ART initiation and effective cervical cancer screening in women living with HIV should be key parts of global efforts to reduce cancer-related health inequities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".