Low-Dose Anti-PD1 Immune Checkpoint Inhibitors in Relapsed/Refractory Hodgkin Lymphoma: A Systematic Review
Bibliographic record
Abstract
PURPOSE: Blockage of PD-1 with drugs such as nivolumab (Nivo) and pembrolizumab (Pembro) has been successfully implemented in the treatment of Hodgkin lymphoma among other types of tumors. Exorbitant costs hinder access for many patients living in low- and middle-income countries (LMICs). Dose reductions on the basis of pharmacodynamic studies have been used to allow access to these drugs to patients with no further options because of economic constraints. In this study, we aimed to systematically review and assess evidence regarding the efficacy and safety of this adapted intervention. METHODS: An expert librarian designed and conducted a literature search in MEDLINE, Scopus, Web of Science, EMBASE, Cochrane Central, and clinicaltrials.gov. Screening was conducted in a title/abstract and full-text phase independently and in duplicate. Primary outcomes were overall survival, overall response rates (ORRs), and incidence of adverse events (AEs). Data extraction was performed independently and in duplicate as well. A qualitative synthesis was performed and reported in the results. RESULTS: Six studies investigated Nivo as an intervention, two Pembro, and 1 both therapies, with a total of 161 patients overall. One study had a single-arm prospective trial design and the rest a retrospective cohort design. ORR ranged from 66% to 100%, with complete response rates between 38% and 75%. The incidence of AEs was mostly high across all studies ranging from 27% to 93%, most of it composed of grade 1-2 AEs (27%-80%). No study reported health-related quality-of-life outcomes. CONCLUSION: Low-dose anti-PD1 immune checkpoint inhibitors have shown to be adequate candidates for testing of their efficacy in randomized trials and could improve access to these high-cost medications for patients with Hodgkin lymphoma both in LMICs and high-income countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.002 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".