Quality Indicators in Otolaryngology–Head and Neck Surgery: A Scoping Review
Bibliographic record
Abstract
ImportanceQuality indicators are used to evaluate the quality of healthcare delivery and as a speciality, otolaryngology-head and neck surgery (OHNS) is beginning to transition toward this empirical understanding of healthcare quality and delivery.ObjectiveTo describe the number and quality of studies that have developed novel quality indicators for any subdiscipline in OHNS.DesignWe performed a database search of MEDLINE (Ovid), EMBASE (Ovid), Web of Science, and Cochrane Database of Systematic Reviews. We did not employ language or study-type restrictions and included studies published from database inception to October 2024.Study SelectionFollowing abstract screening, 184 articles underwent full-text screen. Eligible studies developed quality indicators in any subdiscipline within OHNS. Article screening and full-text review was performed in duplicate.Data Extraction and SynthesisWe extracted study-specific and methodological data in duplicate. Quality appraisal was assessed using the Appraisal of Indicators through Research and Evaluation instrument.ResultsWe identified 10,592 studies, of which 25 studies developed new quality indicators. Quality indicator development studies primarily focused on otology/neurotology, pediatric OHNS, and head and neck surgery. Few studies investigated facial plastics, rhinology and skull base surgery, and laryngology. Most studies employed Delphi consensus methods and patient engagement was rare. Consensus methodology reporting was poor and indicators were often not validated. Outcome indicators were often measured with fewer studies investigation structure or process indicators.ConclusionsQuality indicators may help standardize and improve patient care in OHNS. Future research should focus on structure and process indicators, while improving reporting, optimizing panel composition, and validating quality indicators.
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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 | 0.055 | 0.200 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.038 | 0.037 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".