Low-Dose Anti-PD(L)1 for the Treatment of Solid Malignancies
Bibliographic record
Abstract
PURPOSE Mounting data suggest that lower doses of anti-PD(L)1 agents can be as efficacious as label-approved doses at a fraction of its cost. We compare the outcomes of patients treated with low-dose (LD) and with conventional-dose (CD) anti-PD(L)1 agents. METHODS This observational study evaluates the outcomes of patients with solid malignancies treated with anti-PD(L)1 agents (LD or CD) at Hospital de Base, Brazil. Patients were classified as receiving LD if the dose administered in the first cycle was below the label-approved dose. Efficacy outcomes, including best clinical overall response rate (cORR), clinical progression-free survival (cPFS), and overall survival (OS), were evaluated. RESULTS From January 2020 to May 2023, 71 patients were included: 49 (69%) with LD and 22 (31%) with CD agents. The most frequent tumor sites were the lung (41% LD, 22.9% CD) and skin (melanoma; 24.6% LD, 50% CD). Most of the patients were treated with pembrolizumab (65% LD and 72% CD). The mean dose of pembrolizumab was 95.3 mg (1.5 mg/kg) in LD and 168.7 mg (2.12 mg/kg) in CD groups, once a day, q21d (every 21 days). After a median follow-up of 10.9 months, there were no significant differences between LD versus CD in cORR (38.1% v 35.2%, P = .31), cPFS (5.3 m v 7 m, P = .36), and OS (12.8 m v not reached, P = .17). A subgroup analysis with patients receiving pembrolizumab was performed, and similar results were obtained. CONCLUSION Our study found no differences in cORR, cPFS, and OS between patients treated with LD and CD anti-PD(L)1. LD anti-PD(L)1 could be an alternative to promote accessibility, which warrants further investigation in randomized trials.
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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 | 0.000 | 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) | 0.001 | 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".