Comparison of diagnostic accuracy of computed tomography virtual endoscopy and flexible fibre-optic laryngoscopy in the evaluation of neck anatomic structures and neoplasms
Bibliographic record
Abstract
Background and purpose Computed tomography virtual endoscopy (CT-VE) is a non-invasive technique which allows visualisation of intraluminal surfaces by tridimensional reconstruction of air/soft tissues. The aim of this study was to compare the diagnostic accuracy of CT-VE and flexible fibre-optic laryngoscopy (FFL) in identifying normal neck anatomic structures and pharyngeal and laryngeal lesions. Methods Forty-two patients with a history of neck cancer were assessed by two ENT surgeons using FFL and by one neuroradiologist using CT-VE in order to evaluate the visualisation of the epiglottis, vallecula, glossoepiglottic folds, pyriform sinuses, vocal cords and mass pathology. The visualisation of the structures in both modalities was assessed according to the following score: 0 = not visualised, 1 = partial visualisation, 2 = complete and clear visualisation. A weighted kappa coefficient was used to evaluate the inter-observer agreement. McNemar’s test was performed to compare the two diagnostic tests. Results The inter-observer agreement between FFL and CT-VE was fair in the assessment of the vocal cords ( k = 0.341); moderate in the assessment of the glossoepiglottic folds ( k = 0.418), epiglottis ( k = 0.513) and pyriform sinuses ( k = 0.477); and substantial in the assessment of the vallecula ( k = 0.618) and the tumour (0.740). McNemar’s test showed no significant difference between the two tests ( p<0.05). Conclusion CT-VE is a non-invasive technique with a diagnostic accuracy comparable to FFL in terms of visualisation of anatomical structures and pharyngeal and laryngeal lesions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".