Automated Indentation Mapping of Vocal Fold Structure and Cover Properties Across Species
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: Various animal models have been employed to investigate vocal fold (VF) and phonatory function. However, biomechanical testing techniques to characterize vocal fold structural properties vary and have not compared critical properties across species. We adapted a nondestructive, automated indentation mapping technique to simultaneously quantify VF structural properties (VF cover layer and intact VF) in commonly used species based on the hypothesis that VF biomechanical properties are largely preserved across species. STUDY DESIGN: Ex vivo animal model. METHODS: Canine, leporine, and swine larynges (n = 4 each) were sagittally bisected, measured, and subjected to normal indentation mapping (indentation at 0.3 mm; 1.2 mm/s) with a 2-mm spherical indenter to quantify normal force along the VF cover layer, structural stiffness, and displacement at 0.8 mN; two-dimensional maps of the free VF edge through the conus elasticus were created for these characterizations. RESULTS: Structural stiffness was 7.79 gf/mm (0.15-74.55) for leporine, 2.48 gf/mm (0.20-41.75) for canine, and 1.45 gf (0.56-4.56) for swine. For each species, the lowest values were along the free VF edge (mean ± standard deviation; leporine: 0.40 ± 0.21 gf/mm, canine: 1.14 ± 0.49 gf/mm, swine: 0.89 ± 0.28 gf/mm). Similar results were obtained for the cover layer normal force at 0.3 mm. On the free VF edge, mean (standard deviation) displacement at 0.08 gf was 0.14 mm (0.05) in leporine, 0.11 mm (0.03) in canine, and 0.10 mm (0.02) in swine. CONCLUSIONS: Automated indentation mapping yielded reproducible biomechanical property measurement of the VF cover and intact VF. Divergent VF structural properties across canine, swine, and leporine species were observed. LEVEL OF EVIDENCE: NA Laryngoscope, 129:E26-E31, 2019.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".