Quality Indicators for the Diagnosis and Management of Sudden Sensorineural Hearing Loss
Bibliographic record
Abstract
OBJECTIVE: Sudden sensorineural hearing loss (SSNHL) is an ideal entity for quality indicator (QI) development, providing treatment challenges resulting in variable or substandard care. The American Academy of Otolaryngology-Head and Neck Surgery recently updated their SSNHL guidelines. With SSNHL demonstrating a large burden of illness, this study sought to leverage the updated guidelines and develop QIs that support quality improvement initiatives at an individual, institutional, and systems level. METHODS: Candidate indicators (CIs) were extracted from high-quality SSNHL guidelines that were evaluated using the Appraisal of Guidelines for Research and Evaluation II tool. Each CI and its supporting evidence were summarized and reviewed by a nine-member expert panel based on validity, reliability, and feasibility of measurement. Final QIs were selected from CIs using the modified RAND Corporation-University of California, Los Angeles appropriateness methodology. RESULTS: Fifteen CIs were identified after literature review. After the first round of evaluations, the panel agreed on 11 candidate indicators as appropriate QIs with 2 additional CIs suggested for consideration. An expert panel meeting provided a platform to discuss areas of disagreement before final evaluations. The expert panel subsequently agreed upon 11 final QIs as appropriate measures of high-quality care for SSNHL. CONCLUSION: The 11 proposed QIs from this study are supported by evidence and expert consensus, facilitating measurement across a wide breadth of quality domains. With the recently updated SSNHL guidelines, and a greater focus on quality improvement opportunities, these QIs may be used by healthcare providers for targeted quality improvement initiatives.
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 | 0.097 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".