Incidence and Risk Factors of HIV-Related Non-Hodgkin's Lymphoma in the era of Combination Antiretroviral Therapy: A European Multicohort Study
Bibliographic record
Abstract
BACKGROUND: Incidence and risk factors of HIV-associated non-Hodgkin's lymphoma (NHL) are not well defined in the era of combination antiretroviral therapy (cART). METHODS: A total of 56,305 adult HIV type-1 (HIV-1)-infected patients who started cART in 1 of 22 prospective studies in Europe were included. Weibull random effects models were used to estimate hazard ratios (HRs) for developing systemic NHL and included CD4(+) T-cell counts and viral load as time-updated variables. RESULTS: During the 212,042 person-years of follow-up, 521 patients were diagnosed with systemic NHL and 62 with primary brain lymphoma (PBL). The incidence rate of systemic NHL was 463 per 100,000 person-years not on cART and 205 per 100,000 person-years in treated patients for a rate ratio of 0.44 (95% confidence interval [CI] 0.37-0.53). The corresponding incidence rates of PBL were 57 and 24 per 100,000 person-years (rate ratio 0.43, 95% CI 0.25-0.73). Suppression of HIV-1 replication on cART (HR 0.60, 95% CI 0.44-0.81, comparing < or =500 with 10,000-99,999 copies/ml) and increases in CD4(+) T-cell counts (HR 0.30, 0.22-0.42, comparing > or =350 with 100-199 cells/microl) were protective; a history of Kaposi's sarcoma (HR 1.70, 1.08-2.68, compared to no history of AIDS), transmission through sex between men (HR 1.57, 1.19-2.08, compared with heterosexual transmission) and older age (HR 3.71, 2.37-5.80, comparing > or =50 with 16-29 years) were risk factors for systemic NHL. CONCLUSIONS: The incidence rates of both systemic NHL and PBL were substantially reduced in patients on cART. Timely initiation of therapy is key to the prevention of NHL in the era of cART.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".