Health-related quality of life in cancer patients treated with PD-(L)1 inhibitors: a systematic review
Bibliographic record
Abstract
INTRODUCTION: A systematic review was performed to explore the health-related quality of life (HRQoL) outcomes among cancer patients receiving PD-(L)1 inhibitors compared to those receiving traditional cytotoxic therapy. Areas covered: Citations from PubMed and the American Society of Clinical Oncology meeting library were examined. Cross-references from original studies and review articles were also reviewed. Eligible trials included randomized controlled trials of cancer patients treated with one of the PD-(L)1 inhibitors and reporting HRQoL outcomes. A total of 11 studies were included in the current review. PD-(L)1 inhibitors were associated with a consistent prolongation of the time to symptomatic deterioration. This was shown with the three agents (nivolumab, pembrolizumab, and atezolizumab) as well as across a variety of solid tumors (lung cancer, melanoma, head and neck cancer and urothelial cancer). Moreover, PD-(L)1 inhibitor therapy was associated with better symptomatic control at different follow-up points. This was observed regardless of the agent used of the solid tumor treated. Expert commentary: Across a variety of solid tumor indications as well as a variety of PD-(L)1 inhibitors, the use of PD-(L)1 inhibitors is associated with an improvement in the quality of life. The utility of patient-reported outcomes in predicting clinical benefit from these agents needs to be explored further.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".