Quality Indicators for the Diagnosis and Management of Acute Bacterial Rhinosinusitis
Bibliographic record
Abstract
BACKGROUND: Acute bacterial rhinosinusitis (ABRS) is a highly prevalent disease that is treated by a variety of specialties, including but not limited to, family physicians, emergency physicians, otolaryngology-head and neck surgeons, infectious disease specialists, and allergy and immunologists. Unfortunately, despite high-quality guidelines, variable and substandard care continues to be demonstrated in the treatment of ABRS. OBJECTIVE: This study aimed to develop ABRS-specific quality indicators (QIs) to evaluate the diagnosis and management that reduces symptoms, improves quality of life, and prevents complications. METHODS: A guideline-based approach, proposed by Kötter et al., was used to develop QIs for ABRS. Candidate indicators (CIs) were extracted from 4 guiding documents and evaluated using the Appraisal of Guidelines for Research and Evaluation II (AGREE II) tool. Each CI and its supporting evidence was summarized and reviewed by an expert panel based on validity, reliability, and feasibility of measurement. Final QIs were selected from CIs utilizing the modified RAND/University of California at Los Angeles appropriateness methodology. RESULTS: Twenty-nine CIs were identified after literature review and evaluated by our panel. Of these, 5 CIs reached consensus as being appropriate QIs, with 1 requiring additional discussion. After a second round of evaluations, the panel selected 7 QIs as appropriate measures of high-quality care. CONCLUSION: This study proposes 7 QIs for the diagnosis and management of patients with ABRS. These QIs can serve multiple purposes, including documenting the quality of care; comparing institutions and providers; prioritizing quality improvement initiatives; supporting accountability, regulation, and accreditation; and determining pay for performance 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 | 0.084 | 0.161 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".