Contact endoscopy as a novel technique in the detection and diagnosis of oral cavity and oropharyngeal mucosal lesions in the head and neck
Bibliographic record
Abstract
OBJECTIVE: We aimed to investigate the diagnostic accuracy of contact endoscopy in evaluating oral and oropharyngeal mucosal lesions. METHODS: Between January 2010 and December 2011, 34 patients with lesions of the oral and oropharyngeal mucosa were enrolled in the study. Comparison between initial contact endoscopy results and 'gold standard' tissue biopsy was undertaken. RESULTS: Nine patients had histologically confirmed squamous cell carcinoma, 2 had carcinoma in situ, 3 had dysplastic lesions and 20 patients had various benign lesions. Contact endoscopy demonstrated sensitivity and specificity of 89 and 100 per cent respectively in the evaluation of malignant lesions. Benign lesions were correctly categorised in 50 per cent of cases (10/20). The video images from contact endoscopy could not be interpreted in six cases. CONCLUSIONS: Contact endoscopy demonstrates high sensitivity and specificity in the imaging of malignant lesions with reduced reliability in the evaluation of benign lesions. Significant shortcomings also exist in the design of current technology that we believe represent a significant barrier to the reliable collection of useful video data.
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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 | 0.003 |
| 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 | 0.001 | 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".