Quantifying vocal fold wound‐healing biomechanical property changes
Bibliographic record
Abstract
OBJECTIVES: Development of novel vocal fold (VF) therapeutics is limited by a lack of standardized, meaningful outcomes. We hypothesize that automated microindentation-based VF biomechanical property mapping matched to histology permits quantitative assessment. STUDY DESIGN: Ex vivo. METHODS: Twelve anesthetized New Zealand white rabbits underwent endoscopic right VF injury. Larynges were harvested/bisected day 7, 30, or 60 (n = 4/group), with four uninjured controls. Biomechanical measurements (normal force, structural stiffness, and displacement at 1.96 mN) were calculated using automated microindentation mapping (0.3 mm depth, 1.2 mm/s, 2 mm spherical indenter) with a grid overlay (>50 locations weighted toward VF edge, separated into 14 zones). Specimens were marked/fixed/sectioned, and slides matched to measurement points. RESULTS: In the injury zone, normal force/structural stiffness (mean, standard deviation [SD]/mean, SD) increased from uninjured (2.2 mN, 0.64/7.4 mN/mm, 2.14) and day 7 (2.7 mN, 0.75/9.0 mN/mm, 2.49) to day 30 (4.3 mN, 2.11/14.2 mN/mm, 7.05) and decreased at 60 days (2.7 mN, 0.77/9.1 mN/mm, 2.58). VF displacement decreased from control (0.28 mm, 0.05) and day 7 (0.26 mm, 0.05) to day 30 (0.20 mm, 0.05), increasing at day 60 (0.25 mm, 0.06). A one-way ANOVA was significant; Tukey's post hoc test confirmed day-30 samples differed from other groups (P < 0.05), consistent across adjacent zones. Zones far from injury remained similar across groups (P = 0.143 to 0.551). These measurements matched qualitative histologic variations. CONCLUSION: Quantifiable VF biomechanical properties can be linked to histology. This technological approach is the first to simultaneously correlate functional biomechanics with histology and is ideal for future preclinical studies. LEVEL OF EVIDENCE: NA Laryngoscope, 130:454-459, 2020.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".