The effectiveness of rituximab and HIV on the survival of Ontario patients with diffuse large B‐cell lymphoma
Bibliographic record
Abstract
Abstract Introduction For patients with diffuse large B‐cell lymphoma (DLBCL), standard‐care is rituximab administered with CHOP or CHOP‐like chemotherapy (R‐CHOP). However, the effectiveness and safety of R‐CHOP among DLBCL patients with human immunodeficiency virus (HIV) infection is less clear, as HIV+ patients were omitted from most clinical trials and population‐level data from unselected patients are limited. R‐CHOP was funded for HIV‐associated DLBCL patients with CD4 >50/mm 3 in Ontario in February 2015. Methods Patients with a new diagnosis of DLBCL were identified from the Ontario Cancer Registry between April 2010 and March 2018. HIV diagnosis and chemotherapy regimen were ascertained using administrative databases at Ontario Health. The effect of rituximab and HIV on overall survival was assessed in the HIV+ subgroup (R‐CHOP vs CHOP) and in the R‐CHOP subgroup (HIV+ vs HIV−). Results Among HIV+ patients, receipt of R‐CHOP was associated with a fivefold improvement in overall survival (hazard ratio [HR] 0.29 (0.13‐0.66) compared with CHOP), after adjustment. Among patients who received R‐CHOP (n = 6106), older age, male sex, lower neighborhood income, and higher comorbidity were associated with worse overall survival, after adjustment ( P < .001 for all), but HIV positivity was not prognostic (HR 1.12 (0.60‐2.10)). Within 1‐year after diagnosis, HIV+ patients receiving R‐CHOP had a similar proportion of patients who visited the emergency department (67% vs 66% P = .43) or admitted to hospital (58% vs 52%, P = .43) and as HIV− patients receiving R‐CHOP. Conclusion HIV status did not affect prognosis for patients with DLBCL receiving R‐CHOP in an unselected general population when rituximab was used according to funding criteria. R‐CHOP was safe and effective for DLBCL treatment, regardless of HIV status.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".