Advancing patient-centric care: integrating patient reported outcomes for tolerability assessment in early phase clinical trials – insights from an expert virtual roundtable
Bibliographic record
Abstract
Early phase clinical trials provide an initial evaluation of therapies' risks and benefits to patients, including safety and tolerability, which typically relies on reporting outcomes by investigator and laboratory assessments. Use of patient-reported outcomes (PROs) to inform risks (tolerability) and benefits (improvement in disease symptoms) is more common in later than early phase trials. We convened a two-day expert roundtable covering: (1) the necessity and feasibility of a universal PRO core conceptual model for early phase trials; (2) the practical integration of PROs in early phase trials to inform tolerability assessment, guide dose decisions, or as real-time safety alerts to enhance investigator-reported adverse events. Participants (n = 22) included: patient advocates, regulators, clinicians, statisticians, pharmaceutical representatives, and PRO methodologists working across diverse clinical areas. In this manuscript, we report major recommendations resulting from the roundtable discussions corresponding to each theme. Additionally, we highlight priority areas necessitating further investigation.
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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 | 0.270 | 0.274 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 0.008 |
| 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".