Quality Indicators for the Diagnosis and Management of Menière's Disease
Bibliographic record
Abstract
OBJECTIVE: Menière's disease (MD) is a clinical disorder that often provides challenges in diagnosis and management. High-quality evidence to guide care providers is sparse, which can result in significant practice variations. Quality indicators (QIs) are one method that can be used to standardize and measure accepted care practices to improve healthcare quality and patient outcomes. Here, we developed practical, high-yield QIs that serve to measure and inform the quality of care provided to patients with MD. STUDY DESIGN: Modified RAND Corporation University of California, Los Angeles appropriateness methodology for QI development. SETTING: Multicenter nine-member expert panel. PATIENTS: NA. INTERVENTIONS: NA. MAIN OUTCOME MEASURE: Final QIs deemed appropriate measures of quality care with agreement by the expert panel. RESULTS: Twenty-seven candidate indicators were identified after literature review. After the first round of evaluations, the panel agreed on three candidate indicators as appropriate QIs. A subsequent expert panel meeting provided a platform to discuss disagreements. Two agreed-upon QIs were revised during this discussion before final evaluations. The expert panel ultimately agreed upon five QIs as appropriate measures of high-quality care after completing final evaluations and reviewing updated literature. The five quality indicators measure audiometric documentation, minimization of electrocochleography, use of intratympanic dexamethasone, use of intratympanic gentamycin, and rate of labyrinthectomy/vestibular neurectomy in refractory MD patient. CONCLUSIONS: This study proposes five QIs that cover key aspects of care for MD, such as accurate diagnosis and management options including initial destructive therapies. These QIs can serve multiple purposes, the most important of which is to galvanize quality 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 | 0.109 | 0.199 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".