Routine Surveillance of Chemotherapy Toxicities in Cancer Patients Using the Patient-Reported Outcomes Version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE)
Bibliographic record
Abstract
INTRODUCTION: Systematic documentation of chemotoxicities in outpatient clinics is challenging. Incorporating patient-reported outcome (PRO) measures in clinical workflows can be an efficient strategy to strengthen the assessment of symptomatic treatment toxicities in oncology clinical practice. We compared the adequateness, feasibility, and acceptability of toxicity documentation using systematic, prospective, application of the PRO Common Toxicity Criteria for Adverse Events (PRO-CTCAE) tool. METHODS: At a comprehensive cancer center, data abstraction of electronic health record reviews elucidated current methods and degree of chemotoxicity documentation. Web-based 32-item PRO-CTCAE questionnaires, administered in ambulatory clinics of patients receiving chemotherapy, captured chemotoxicities and respective severities. Patient telephone surveys assessed whether healthcare providers had addressed chemotoxicities to the patients' satisfaction. RESULTS: Over a broad demographic of 497 patients receiving chemotherapy, 90% (95% CI 84-96%) with significant chemotoxicities (n = 107) reported that their providers had discussed toxicities with them; of these, 70% received a therapy management change, while among the rest, 17% desired a change in management. Of patients surveyed, 91% (95% CI 82-99%) were satisfied with their current chemotoxicity management. Clinician chart documentation varied greatly; descriptors rather than numerical grading scales were typically used. Although 93% of patients were willing to complete the PRO survey, only 50% thought that it would be acceptable to complete this survey at routine clinic visits. CONCLUSION: Use of PRO-CTCAE in routine clinical practice promotes systematic evaluation of symptomatic toxicities and improves the clarity, consistency, and efficiency of clinician documentation; however, methods to improve patient willingness to complete this tool routinely are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".