Understanding the Role of Patient-Reported Outcomes for Decision-Making in Early-Phase Dose-Finding Clinical Trials
Bibliographic record
Abstract
In early-phase dose-finding clinical trials, integrating patient-reported outcomes (PROs) is essential for enhancing patient-centered decision-making. This short communication advocates for several key practices to achieve such integration. Firstly, foster patient-centered communication that ensures patient understanding of the potential benefits of early-phase trials, thereby mitigating therapeutic misconceptions. Secondly, (a) facilitate partnerships to understand and address the underlying reasons for discrepancies between clinician and patient reports of adverse events and (b) facilitate partnerships among clinical trialists, statisticians, clinicians, patients, and advocates to gain diverse perspectives of adverse events and in so doing ensure that patients comprehend how their data will be used. Thirdly, optimize trial design and data collection by (a) determining optimal and feasible frequencies for PRO collection to minimize patient burden while maintaining data integrity and (b) effectively incorporating concordant PROs to guide dose recommendation decisions and adapt trial designs and statistical methods accordingly. Future research will involve investigating the application of these practices in patients within the Virginia Commonwealth University (VCU) Massey Comprehensive Cancer Center Catchment Area. By integrating these recommendations, early-phase dose-finding clinical trials have the potential to achieve more informed and patient-centered objectives.
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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 | 0.573 | 0.769 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.019 | 0.022 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".