A tailored phase I-specific patient-reported outcome (PRO) survey to capture the patient experience of symptomatic adverse events
Bibliographic record
Abstract
BACKGROUND: Patient perspectives are fundamental to defining tolerability of investigational anti-neoplastic therapies in clinical trials. Phase I trials present a unique challenge in designing tools for efficiently collecting patient-reported outcomes (PROs) given the difficulty of anticipating adverse events of relevance. However, phase I trials also offer an opportunity for investigators to optimize drug dosing based on tolerability for future larger-scale trials and in eventual clinical practice. Existing tools for comprehensively capturing PROs are generally cumbersome and are not routinely used in phase I trials. METHODS: Here, we describe the creation of a tailored survey based on the National Cancer Institute's PRO-CTCAE for collecting patients' perspectives on symptomatic adverse events in phase I trials in oncology. RESULTS: We describe our stepwise approach to condensing the original 78-symptom library into a modified 30 term core list of symptoms which can be efficiently applied. We further show that our tailored survey aligns with phase I trialists' perspectives on symptoms of relevance. CONCLUSIONS: This tailored survey represents the first PRO tool developed specifically for assessing tolerability in the phase I oncology population. We provide recommendations for future work aimed at integrating this survey into clinical practice.
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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 | 0.025 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".