Associations of CD4 Cell Count Measures With Infection-Related and Infection-Unrelated Cancer Risk Among People With HIV
Bibliographic record
Abstract
BACKGROUND: People with HIV are at higher risk of infection-related cancers than the general population, which could be due, in part, to immune dysfunction. Our objective was to examine associations between 4 CD4 count measures as indicators of immune function and infection-related and infection-unrelated cancer risk. SETTING: We conducted a cohort study of adults with HIV who were diagnosed with cancer in Ontario, Canada. Incident cancers were identified from January 1, 1997 to December 31, 2020. METHODS: We estimated adjusted hazard ratios (aHR) for the associations between CD4 measures (baseline CD4, nadir CD4, time-updated CD4, time-updated CD4:CD8) and cancer incidence rates using competing risk analyses, adjusted for socio-demographic factors, history of hepatitis B or C infection, baseline viral load, smoking, and alcohol use. RESULTS: Among 4771 people with HIV, contributing 59,111 person-years of observation, a total of 549 cancers were observed. Low baseline CD4 (<200 cells/µL) (aHR 2.08 [95% CI: 1.38 to 3.13], nadir (<200 cells/µL) (aHR 2.01 [95% CI: 1.49 to 2.71]), low time-updated CD4 (aHR 3.52 [95% CI: 2.36 to 5.24]) and time-updated CD4:CD8 ratio (<0.4) (aHR 2.02 [95% CI: 1.08 to 3.79]) were associated with an increased rate of infection-related cancer. No associations were observed for infection-unrelated cancers. CONCLUSIONS: Low CD4 counts and indices were associated with increased rates of infection-related cancers among people with HIV, irrespective of the CD4 measure used. Early diagnosis and linkage to care and high antiretroviral therapy uptake may lead to improved immune function and could add to cancer prevention strategies such as screening and vaccine uptake.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".