A framework for quality measurement in the presurgical care of chronic rhinosinusitis: a review from the Quality Improvement Committee of the American Rhinologic Society
Bibliographic record
Abstract
BACKGROUND: Quality improvement (QI) in the management of chronic rhinosinusitis (CRS) is garnering increasing attention. Defining frameworks and metrics to assess the quality of key components in CRS management could assist in reducing unwarranted practice variation and increase high-quality care. METHODS: A panel of the American Rhinologic Society (ARS) QI committee reviewed the literature to determine important presurgical components of CRS care that warrant QI. The evidence was organized into 4 categories: (1) diagnosis, (2) medical management, (3) appropriate patient selection for surgery, and (4) patient-centered discussion. The combination of these categories was used to develop a framework termed the CRS Appropriate Presurgical Algorithm (CAPA). RESULTS: Prior to offering surgery for CRS, the best available evidence support the following quality metrics: (1) a guideline-based diagnosis should be confirmed; (2) appropriate medical management, including a minimum of topical corticosteroid therapy and saline irrigations, should have been attempted (assuming patient tolerance); (3) a computed tomography (CT) scan should be obtained (to confirm the presence of sinus inflammation and for surgical planning); and (4) a patient-centered discussion regarding treatment options for refractory CRS (ie, alternative medical therapies vs surgery vs observation) while focusing on risks and benefits, the need for long-term medical compliance, and understanding of patient preferences and expectations. CONCLUSION: Defining metrics that assess key components to CRS care prior to offering surgery has the potential to further improve upon an already successful treatment paradigm, reduce unwarranted practice variation, and to ensure that patients are receiving a similar level of high-quality care.
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.
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 | 0.144 | 0.158 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.010 |
| 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".