Quality measurement for rhinosinusitis: a review from the Quality Improvement Committee of the American Rhinologic Society
Bibliographic record
Abstract
BACKGROUND: Measuring quality outcomes is an important prerequisite to improve quality of care. Rhinosinusitis represents a high value target to improve quality of care because it has a high prevalence of disease, large economic burden, and large practice variation. In this study we review the current state of quality measurement for management of both acute (ARS) and chronic rhinosinusitis (CRS). METHODS: The major national quality metric repositories and clearinghouses were queried. Additional searches included the American Academy of Otolaryngology-Head and Neck Surgery database, PubMed, and Google to attempt to capture any additional quality metrics. RESULTS: Seven quality metrics for ARS and 4 quality metrics for CRS were identified. ARS metrics focused on appropriateness of diagnosis (n = 1), antibiotic prescribing (n = 4), and radiologic imaging (n = 2). CRS quality metrics focused on appropriateness of diagnosis (n = 1), radiologic imaging (n = 1), and measurement of patient quality of life (n = 2). The Physician Quality Reporting System (PQRS) currently tracks 3 ARS quality metrics and 1 CRS quality metric. There are no outcome-based rhinosinusitis quality metrics and no metrics that assess domains of safety, patient-centeredness, and timeliness of care. CONCLUSIONS: The current status of quality measurement for rhinosinusitis has focused primarily on the quality domain of efficiency and process measures for ARS. More work is needed to develop, validate, and track outcome-based quality metrics along with CRS-specific metrics. Although there has been excellent work done to improve quality measurement for rhinosinusitis, there remain major gaps and challenges that need to be considered during the development of future metrics.
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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 | 0.059 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| 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".