Viability of Whole Tissue Microbiopsy (WTM) for the Study and Management of Oral Leukoplakia
Bibliographic record
Abstract
Introduction: Leukoplakia is the most frequent potentially malignant disorder. Management and diagnosis requires clinical and histopathogical monitorization. Conventional biopsy generates patient morbidity and is considered a complex procedure for general dentists, which can delay initial diagnosis. To solve these problems, we have proposed a novel procedure denominated Whole Tissue Microbiopsy (WTM). The aim of this study is to evaluate the samples obtained with the WTM procedure and to test their viability; to check if they are applicable in all anatomic locations and compare the results with those obtained with conventional biopsy. Methods: We studied 41 clinically compatible lesions with oral leukoplakia. A tissue sample was taken using the WTM technique, after which, a conventional biopsy was performed on the same location. Both samples were studied and compared in terms of viability and concordance. Results: 100% of the samples obtained using the WTM procedure were viable. 95% of the samples were useful to detect dysplasia, and in 85% of cases the basal membrane was retained. Coincidence with conventional biopsy as to detect cancer-dysplasia was 78% and showed a 53.8% sensitivity regarding the detection of dysplasia-Cancer. Discussion and Conclusion: The samples obtained by the WTM are viable for study. Conservation of all epithelial layers in the sample and the basement membrane in particular is not influenced by the anatomical area or by the clinical appearance of the lesion. The results that did not coincide with the conventional biopsy were due to the difference in size and not the quality of it.
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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 | 0.002 |
| 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 | 0.001 |
| 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".