Trends in the Incidence of HIV-Related Hematologic Malignancies in the Era of Combination Antiretroviral Therapy and Predictors of HIV-Related Non-Hodgkin's Lymphoma Development and Survival.
Bibliographic record
Abstract
Abstract Abstract 1916 Poster Board I-939 Introduction: Since the introduction of combination antiretroviral therapy (cART), the incidence rates of non-Hodgkin's lymphoma (NHL) and primary central nervous system lymphoma (PCNSL) have declined; however, less is known about the rates of other hematologic malignancies such as Hodgkin lymphoma (HL) and multiple myeloma (MM). We aimed to study changes in the incidence and outcomes of hematologic malignancies (HMs) in the pre- and post-cART eras. Methods: A retrospective analysis of The Ontario HIV Treatment Network Cohort Study (OCS) was performed. The OCS is an ongoing prospective study of HIV-infected adults from 11 sites throughout Ontario, Canada. Incidence rates of HMs were calculated for the pre- (<1997) and post-cART (≥ 1997) eras and compared using Poisson regression analysis. Median survival for each HM was calculated using Kaplan Meier techniques and compared using the logrank test. Predictors of NHL and death from NHL including age, sex, CD4 count, viral load, previous AIDS-defining illness, cART era and duration of HIV infection were evaluated using Cox proportional hazard models. All variables except sex were considered time dependent variables. Results: The OCS database included 4118 individuals with 41978 person-years of follow up over 28 years (1980-2008). There was no significant difference in the incidence of HM in the pre- and post-cART eras (3.6 versus 4.1 cases per 1000 person-years, p-value=0.49) although incidence of PCNSL trended downward (0.8 versus 0.4 cases per 1000 person-years, p-value=0.13) and incidence of HL trended upward (0.1 versus 0.4 cases per 1000 person-years, p-value=0.08). Those with HL had the longest median survival, followed by NHL and PCNSL (63, 39 and 4 months respectively). Predictors of NHL development included low CD4 count, high viral load and pre-cART era. Predictors of death following NHL diagnosis were low CD4 count, previous AIDS-defining illness and longer duration of HIV infection. Conclusion: Since the introduction of cART, the overall incidence of HM has not significantly changed in this cohort. However, as fewer individuals in the cART era develop low CD4 counts, high viral loads and AIDS-defining illnesses, reduced incidence of NHL in this cohort and improved survival following NHL may become apparent. Disclosures: No relevant conflicts of interest to declare.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".