Integrating Patient-Reported Outcomes Into the Care of People With Advanced Cancer—A Practical Guide
Bibliographic record
Abstract
Patient-reported outcomes (PROs) are being increasingly integrated into routine clinical practice to enhance individual patient care. This has been driven by recognition of the benefits of PROs in enhancing symptom management, patient satisfaction, quality of life, and overall survival, and reductions in acute health care utilization. These benefits are reflected in the emergence of value-based health care initiatives incorporating PRO symptom monitoring such as the Enhancing Oncology Model in the United States. However, implementing PROs can be challenging and it can be difficult to know where to begin to select appropriate PROs, and effectively display and appropriately interpret PRO data. This manuscript summarizes an educational session at the 2024 ASCO Annual Meeting, which provided practical guidance to clinicians seeking to incorporate PROs into the care of people with advanced cancer. We focus on why it is important to collect PROs in routine care from a patient's perspective, how to select PROs for symptom monitoring (including using static patient-reported outcome measures and newer item libraries), and highlight key pearls and pitfalls in the display and interpretation of PROs. We highlight the breadth of existing resources available to guide clinicians in PRO implementation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".