Stakeholder Perceptions of Key Aspects of High-Quality Cancer Care to Assess with Patient Reported Outcome Measures: A Systematic Review
Bibliographic record
Abstract
Performance measurement is the process of collecting, analyzing, and reporting standardized measures of clinical performance that can be compared across practices to evaluate how well care was provided. We conducted a systematic review to identify stakeholder perceptions of key symptoms and health domains to test as patient-reported performance measures in oncology. Stakeholders included cancer patients, caregivers, clinicians, and healthcare administrators. Standard review methodology was used, consistent with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). MEDLINE/PubMed, EMBASE, and the Cochrane Library were searched to identify relevant studies through August 2020. Four coders independently reviewed entries and conflicts were resolved by a fifth coder. Efficacy and effectiveness studies, and studies focused exclusively on patient experiences of care (e.g., communication skills of providers) were excluded. Searches generated 1813 articles and 1779 were coded as not relevant, leaving 34 international articles for extraction. Patients, caregivers, clinicians, and healthcare administrators prioritize psychosocial care (e.g., distress) and symptom management for patient-reported performance measures. Patients and caregivers also perceive that maintaining physical function and daily activities are critical. Clinicians and administrators perceive control of specific symptoms to be critical (gastrointestinal symptoms, pain, poor sleep). Results were used to inform testing at six US cancer centers.
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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 | 0.039 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".