Rhinology‐specific priority setting for quality improvement: a modified Delphi study from the Quality Improvement Committee of the American Rhinologic Society
Bibliographic record
Abstract
BACKGROUND: Improving the quality of healthcare is a complex and resource intensive process. To optimize the allocation of scarce resources, quality improvement (QI) should focus on high-value diseases that will produce the largest improvement in health system performance. Given the breadth and multidisciplinary nature of sinonasal disease management, the purpose of this study was to transparently develop a prioritized list of sinonasal diseases for QI from the perspective of the specialty of rhinology and the American Rhinologic Society (ARS). METHODS: The RAND modified Delphi methodology was used to rank the priority of nine sinonasal disease categories from 1 (lowest priority) to 9 (highest priority). Two rounds of ranking along with a teleconference meeting was performed by a panel of 9 experts from the ARS Quality Improvement Committee. RESULTS: The final QI-prioritized list of sinonasal diseases are as follows: chronic rhinosinusitis (CRS) (mean score = 8.9), recurrent acute rhinosinusitis (RARS) (mean score = 7.9), sinonasal neoplasms (mean score = 7.0), anatomic nasal obstruction (mean score = 5.9), refractory epistaxis (mean score = 5.2), complicated acute rhinosinusitis (mean score = 5.2), chronic nonallergic rhinitis (mean score = 4.4), orbital disease (mean score = 4.3), uncomplicated acute rhinosinusitis (mean score = 4.1), and allergy/allergic rhinitis (mean score = 3.7). CONCLUSION: The three most important disease categories for QI from the perspective of the specialty of rhinology were CRS, RARS, and sinonasal neoplasms. Future studies need to define and validate quality metrics for each of these important disease categories in order to facilitate appropriate measurement and improvement initiatives.
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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 | 0.179 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".