Deep brush‐based cytology in tonsils resected for benign diseases
Bibliographic record
Abstract
A fraction of oropharyngeal cancer (OPC), especially in the tonsil, is caused by human papillomavirus (HPV), mainly HPV16. Noninvasive diagnostic methods to detect precancerous lesions in the tonsil would be useful, e.g., liquid-based cytology (LBC). However, ill-characterized precancerous lesions may be hidden in the depth of the tonsillar crypts. We therefore conducted a study on HPV and tonsillar precancerous lesions to evaluate, among other things, the utility of LBC obtained by deep brushing of the resected tonsils. Two hundred non-paediatric patients (mean age: 30.3 years) who underwent tonsillectomy for infection-related conditions (69%) or other conditions (mainly obstructive sleep apnoea, 31%) were included. An ultra-sensitive Luminex bead-based platform was used to test for the DNA of 21 mucosal HPV types; 56% of slides were unsatisfactory due to low number of squamous epithelial cells or the masking effect of a large number of lymphocytes. Three patients (1.5%; 95% CI: 0.5-4.3) showed suspicious cytological findings (atypical squamous cells-cannot exclude high-grade squamous intraepithelial lesion, ASC-H) while 3 others were HPV-positive (2 for HPV16 and 1 for HPV39). None of the ASC-H patients and HPV-positive patients showed dysplasia at histological examination. The rarity of HPV infection in the tonsil conflicts with the relatively frequent detection of the virus in the mouth. In conclusion, aggressive deep brushing of tonsils, while hardly applicable in vivo, is unlikely to be a reliable method to detect precancerous lesions. The absence of OPC screening modalities places the priority on multi-purpose primary prevention strategies, i.e., HPV vaccination and reduction of smoking and drinking.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".