Multi‐institutional Validation of the Interarytenoid Assessment Protocol for Pediatric Laryngeal Cleft
Bibliographic record
Abstract
BACKGROUND: Current assessment techniques for determining whether a patient has normal interarytenoid anatomy, a deep interarytenoid notch, or a minor laryngeal cleft are highly variable. However, differentiating between these three entities is important, given it may distinguish whether a patient should be considered for surgical intervention. The Interarytenoid Assessment Protocol (IAAP) was developed to provide standardization of interarytenoid anatomy evaluations. We aimed to assess the reliability of the IAAP for assessment of interaytenoid mucosal height (IAMH) through a multi-institutional validation study. METHODS: Reliability of the IAAP was assessed by 10 pediatric otolaryngologists all from different academic centers. 30 de-identified endoscopic videos of interarytenoid assessments were rated at two separate time points, 2 months apart. Intra-class correlation (ICC) coefficients with two-way models were used to evaluate inter- and intra-rater reliability. RESULTS: Thirty endoscopic videos were collected for patients with a median (IQR) age of 4.9 years (59 months; range: 1 month to 20 years). On the first video assessment, inter-rater reliability was 0.74 (95% CI 0.63-0.84), and on the second video assessment, inter-rater reliability was 0.75 (95% CI 0.63-0.85) indicating strong inter-rater reliability. Overall intra-rater test-retest reliability was 0.75 (95% CI 0.69-0.79) indicating strong agreement. In almost half, 14 (46.6%) raters chose IAAP classification levels within 1 level of each other. CONCLUSIONS: Multi-institutional validation of the IAAP demonstrates strong inter- and intra-rater reliability for assessment of IAMH when evaluated through pictorial analysis. Standardization of anatomical evaluations may improve our ability to perform more reliable outcomes studies of pediatric pharyngeal dysphagia in the future. LEVEL OF EVIDENCE: NA Laryngoscope, 135:1499-1505, 2025.
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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 | 0.086 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".