High-Priority Topics for Cancer Quality Measure Development: Results of the 2012 American Society of Clinical Oncology Collaborative Cancer Measure Summit
Bibliographic record
Abstract
PURPOSE: Most cancer quality measures focus on individual cancers, assess specific providers, and evaluate processes of care. Although important, these efforts are not sufficient. A more comprehensive measure set is needed to address gaps in care, focus on patients rather than providers, and assess the cross-cutting aspects of care that are relevant to all patients with cancer throughout the trajectory of their illness. METHODS: With the long-term goal of developing a more comprehensive oncology measure set, the American Society of Clinical Oncology (ASCO) organized a collaborative measure summit that used an iterative consensus approach to identify priorities for the development of new cancer quality measures. The summit, which included professional societies and patient/consumer advocacy organizations, was held during the ASCO Quality Care Symposium in December 2012. RESULTS: This effort, which brought together 12 diverse stakeholders, identified 10 high-priority topics for cancer quality measure development that cross-cut cancer diagnoses and care settings and addressed patient-centered concerns. Topics of particular interest included planning and counseling before therapy, interdisciplinary and multidisciplinary coordinated care, comprehensive symptom assessment, patient experience of care, and use of palliative care and hospice services. CONCLUSION: This is an important first step in the development of patient-centered, cross-cutting cancer quality measures. Addressing the high-priority topics identified by this effort will help fill the gaps left by existing cancer quality measures, including care coordination and transitions, quality of life, safety, experience of care, and outcomes. More work will be needed to specify, implement, and validate measures based on these topics.
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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 | 0.243 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".