Advancing Performance Measurement in Oncology: Quality Oncology Practice Initiative Participation and Quality Outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The American health care system, including the cancer care system, is under pressure to improve patient outcomes and lower the cost of care. Government payers have articulated an interest in partnering with the private sector to create learning communities to measure quality and improve the value of health care. In 2006, the American Society for Clinical Oncology (ASCO) unveiled the Quality Oncology Practice Initiative (QOPI), which has become a key component of the measurement system to promote quality cancer care. QOPI is a physician-led, voluntary, practice-based, quality-improvement program, using performance measurement and benchmarking among oncology practices across the United States. Since its inception, ASCO's QOPI has grown steadily to include 973 practices as of November 2010. One key area that QOPI has addressed is end-of-life care. During the most recent data collection cycle in the Fall of 2010, those practices completing multiple data collection cycles had better performance on care of pain compared with sites participating for the first time (62.61% v 46.89%). Similarly, repeat QOPI participants demonstrated meaningfully better performance than their peers in the rate of documenting discussions of hospice and palliative care (62.42% v 54.65%) and higher rates of hospice enrollment. QOPI demonstrates how a strong performance measurement program can lead to improved quality and value of care for patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.057 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it