Characterizing Vocal Fold Injury Recovery in a Rabbit Model With <scp>Three‐Dimensional</scp> Virtual Histology
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: In animal studies of vocal fold scarring and treatment, imaging-based evaluation is most often conducted by tissue slicing and histological staining. Given variation in anatomy, injury type, severity, and sacrifice timepoints, planar histological sections provide limited spatiotemporal details of tissue repair. Three-dimensional (3D) virtual histology may provide additional contextual spatial information, enhancing objective interpretation. The study's aim was to evaluate the suitability of magnetic resonance imaging (MRI), microscale computed tomography (CT), and nonlinear laser-scanning microscopy (NM) as virtual histology approaches for rabbit studies of vocal fold scarring. METHODS: A unilateral injury was created using microcup forceps in the left vocal fold of three New Zealand White rabbits. Animals were sacrificed at 3, 10, and 39 days postinjury. ex vivo imaging of excised larynges was performed with MRI, CT, and NM modalities. RESULTS: The MRI modality allowed visualization of injury location and morphological internal features with 100-μm spatial resolution. The CT modality provided a view of the injury defect surface with 12-μm spatial resolution. The NM modality with optical clearing resolved second-harmonic generation signal of collagen fibers and two-photon autofluorescence in vocal fold lamina propria, muscle, and surrounding cartilage structures at submicrometer spatial scales. CONCLUSIONS: Features of vocal fold injury and wound healing were observed with MRI, CT, and NM. The MRI and CT modalities provided contextual spatial information and dissection guidance, whereas NM resolved extracellular matrix structure. The results serve as a proof of concept to motivate incorporation of 3D virtual histology techniques in future vocal fold injury animal studies. LEVEL OF EVIDENCE: NA Laryngoscope, 131:1578-1587, 2021.
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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 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".