Quality of patient‐reported outcomes in oncology clinical trials using immune checkpoint inhibitors: A systematic review
Bibliographic record
Abstract
BACKGROUND: There are limited data regarding the quality of patient-reported outcome (PRO) data in immune checkpoint inhibitor (ICI) clinical trial publications. METHODS: A systematic search of citations from various databases was conducted to identify prospective clinical trials involving ICI in advanced tumors from 2003 to 2020. A 30-point score was adapted from the CONSORT PRO extension statement to assess adherence to CONSORT PRO reporting. Linear regression was used to identify factors associated with quality reporting. RESULTS: After the review of 8058 articles, 33 trials were included with ICIs as either monotherapy (91%) or part of a combination regimen (9%). The median score was 23.5 points (range 15-29). In the majority of cases (82%), PROs were reported in a separate publication from the original study. Most of the trials were conducted in the metastatic setting and predominantly in melanoma, lung, and renal cell carcinoma (RCC) (73%). Univariate analysis revealed that trials with greater than 250 patients were associated with a higher score. The score was more likely to be lower in disease sites other than melanoma, lung, and RCC and was higher in the KEYNOTE than in the CHECKMATE trial series. There was no significant correlation between the score and whether a trial met its primary end-point or if the trial improved or worsened the quality of life. In the multivariate analysis, the number of patients enrolled to the trial, disease site, and trial series remained significant. CONCLUSIONS: The quality of reporting of PROs in ICI phase II and III clinical trials is heterogeneous across various cancer sites. As PRO data are increasingly used to counsel patients and complement clinical decision making, innovative and collaborative efforts are required to improve the reporting of these essential data.
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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 | 0.085 | 0.298 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".