Patient-reported outcome instruments used in immune-checkpoint inhibitor clinical trials in oncology: a systematic review
Bibliographic record
Abstract
CONTEXT: Immune-checkpoint inhibitors (ICI) have shown significant benefits for overall survival across various cancer types. Patient-reported outcomes (PROs) are assessed in clinical trials as a measure of efficacy. However, it remains unclear to what extent current PRO instruments capture symptoms specific to ICI toxicities. We conducted a systematic review to identify the use and content validity of PRO instruments in ICI clinical trials in oncology. METHODS: Literature was retrieved from PubMed, Embase, PsycINFO, Medline and CINAHL databases. Articles presenting ICI clinical trials' PRO results, clinical trial study protocols, and conference abstracts stating the use of PRO measures were assessed. We evaluated the validity of identified instruments by comparing their symptom-related content with the adverse events reported in each ICI clinical trial. RESULTS: From database inception until January 2020, we identified 191 ICI clinical trials stating the use of PRO measures of which 26 published PRO results. The cancer-specific EORTC QLQ-C30 and the generic EQ-5D questionnaires were the most widely used instruments, often in combination with disease-specific PROs. Instruments used to report PRO symptom-related toxicities covered 45% of the most frequently reported AEs, whereas 23% of AEs were partially covered and 29% were not covered at all. Of non-covered AEs, 59% referred to the dermatologic system. Partially covered AEs related to endocrine and specific types of pain. CONCLUSION: Despite the high frequency of symptom-related toxicities related to ICI, these events are only partially covered (or not addressed) by current PRO instruments, even when combined. Further research is needed to develop new strategies to tailor PRO instruments to specific ICI toxicities.
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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 | 0.056 | 0.234 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".