Multimodal virtual histology of rabbit vocal folds by nonlinear microscopy and nano computed tomography
Bibliographic record
Abstract
Human vocal folds (VFs) possess a unique anatomical structure and mechanical properties for human communication. However, VFs are prone to scarring as a consequence of overuse, injury, disease or surgery. Accumulation of scar tissue on VFs inhibits proper phonation and leads to partial or complete loss of voice, with significant consequences for the patient's quality of life. VF regeneration after scarring provides a significant challenge for tissue engineering therapies given the complexity of tissue microarchitecture. To establish an effective animal model for VF injury and scarring, new histological methods are required to visualize the wound repair process of the tissue in its three-dimensional native environment. In this work, we propose the use of a combination of nonlinear microscopy and nanotomography as contrast methods for virtual histology of rabbit VFs. We apply these methods to rabbit VF tissue to demonstrate their use as alternatives to conventional VF histology that may enable future clinical studies of this injury model.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".