Cancer risk and temporal trends in people with HIV during a quarter of a century – a nationwide population-based matched cohort study
Bibliographic record
Abstract
BACKGROUND: It is important to understand current trends in cancer risk among people living with HIV (PLWH) to improve outcomes and to commission and delivery appropriate services. METHODS: Nationwide, population-based, matched cohort study on all adult PLWH treated at Danish HIV health care centres since 1 January 1995 and a comparison cohort, randomly selected from the background population and matched on sex and date of birth. RESULTS: We included 6327 PLWH and 63,270 individuals in the comparison cohort - 74% were men and median age was 37 (interquartile range: 30-46). For both smoking related cancers, virological cancers and other cancers, incidence was substantially higher in the first year of observation for PLWH than for the remaining observation period. The risk of smoking related cancer remained stably increased throughout the observation period, whereas the relative risk of virological cancers decreased, especially in the first year of follow up. Finally, the risk of other cancers for PLWH decreased to a level below that of the background population during the study period. CONCLUSION: The fact that the risk of other cancers was probably not higher among PLWH than in the comparison cohort is encouraging, as the excess risk of virological and smoking related cancers is potentially preventable by timely treatment of HIV and smoking cessation.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".