3-D optical coherence tomography of the laryngeal mucosa*
Bibliographic record
Abstract
Laryngeal carcinoma is one of the commonest primary head and neck malignancy and the need for early identification is very important for successful treatment. Outpatient fibreoptic examination of the larynx is unreliable in differentiating benign, pre-malignant and malignant lesions, and therefore surgeons have to rely on biopsies for a definitive diagnosis. This is an invasive procedure requiring general anaesthesia and may have a detrimental effect on the patient's voice. Conventional imaging modalities (ultrasound, computed tomography and magnetic resonance imaging) have a limited resolution and hence cannot give sufficient information on the extent or nature of laryngeal lesions. The aim of our study is to investigate the feasibility of optical coherence tomography (OCT) in imaging the normal larynx, to lay the foundations for an investigation of its ability to differentiate between benign and malignant disease. Ten tissue specimens from normal larynges were imaged with an 850 nm OCT system that was capable of providing both B-scan (longitudinal or cross-section) images as well as C-scan (en-face or images at constant depth). The en-face OCT mode allowed us to reconstruct 3-D OCT images of the tissue examined. Imaged specimens were processed with standard histopathological techniques and sectioned in the plane of the B-scan OCT images. Haematoxylin-eosin stained specimens were compared with the OCT images thus collected. Preliminary results showed good correlation between OCT images and histology sections in normal tissue.
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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 | 0.000 | 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 | 0.000 |
| 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 | 0.000 |
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".