Development and testing of patient-reported outcome performance measures (PRO-PMs) for oncology practice.
Bibliographic record
Abstract
173 Background: Symptom management is a cornerstone of quality oncology practice. ASCO established a Working Group to develop patient-reported outcome performance measures (PRO-PMs) for assessing symptom management. We describe multi-center testing funded by PCORI. Methods: Multi-stakeholder consensus and literature review identified 11 symptoms for testing as potential PRO-PMs. For these symptoms, questions from the NCI’s PRO-CTCAE tool were administered at 6 US academic and community oncology practices. Patients across cancer types completed questions electronically on days 5-15 of chemotherapy cycles. PRO-CTCAE mapped scores were dichotomized to delineate clinically meaningful thresholds (0-1 vs ≥2), and rates were tabulated between practices. Symptoms were selected to become PRO-PMs if clinically actionable and with prevalence ≥20%; between-practice variation was evaluated using χ2. Twelve candidate sociodemographic and clinical risk adjustment (RA) variables were evaluated via Akaike information criterion testing. Risk-adjusted PRO-PM rates were calculated using observed:expected ratios via generalized linear mixed modeling. Results: Among 653 enrolled patients, 607 (93%) completed questionnaires. Four of 11 symptoms met criteria for PRO-PM development: nausea, constipation, insomnia, pain. Four RA variables met inclusion criteria: age, gender, cancer type, insurance type. The Table shows raw and risk-adjusted rates of symptom burden (scores ≥2) for each PRO-PM across practices. Risk-adjustment yielded a modest impact on scores. Conclusions: Oncology PRO-PMs have been developed to quantify the burden of actionable symptoms at the practice level. Collection from patients is feasible. Further refinement is underway prior to submission for endorsement by the National Quality Forum. [Table: see text]
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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 | 0.148 | 0.240 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".