Accuracy of flexible versus rigid laryngoscopic photo-documentation in the diagnosis of early glottic cancer
Bibliographic record
Abstract
OBJECTIVE: To compare the image quality provided by rigid laryngoscopes versus flexible distal-chip laryngoscopes when documenting the same laryngeal pathology. METHODS: This paper reports a prospective single-blind study. Ten early stage glottic cancer cases were selected. Photographs of the pathologies were taken using both rigid and flexible distal-chip laryngoscopes (a total of 20 photographs). Nineteen clinicians were asked to review the laryngoscopic photographs; the clinicians were provided with a worksheet, which included questions regarding the clinical description, photograph quality and overall satisfaction with the images obtained. Clinicians’ responses to the worksheet questions were then analysed. RESULTS: The overall accuracy rate for lesion sidedness, anatomical sub-site involvement, anterior commissure involvement and tumour staging were 94.7 per cent, 46.6 per cent, 53.7 per cent and 47.1 per cent respectively. There were no statistically significant differences in terms of the accuracy rates, photograph quality or overall satisfaction with the photographs obtained by either modality. CONCLUSION: There were no statistically significant differences demonstrated in overall clinical accuracy or perceived image quality between the use of the rigid or flexible endoscopes when interpreting images of early glottic cancer.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".