Quality Evaluation of Cone Biopsy Specimens Obtained by Large Loop Excision of the Transformation Zone
Bibliographic record
Abstract
BACKGROUND: Large loop excision of the transformation zone (LLETZ) has been used for the diagnosis and treatment of precancerous cervical lesions, and it is the first choice of treatment in the majority of cervical pathology services. The aim of this study was to evaluate the presence of thermal artifacts, the need for serial sections, the percentage of clear and involved resection margins and the relationship between endocervical gland involvement and the severity of the lesion in samples resected using LLETZ. METHODS: A retrospective study was performed at Santa Casa de Misericordia School of Science (HSCMV), Vitoria, Espirito Santo, Brazil with a sample of 52 histopathology slides from patients submitted to conization because of abnormal cytology findings and a biopsy result of cervical intraepithelial neoplasia (CIN) 2, CIN 3 and adenocarcinoma in situ. Statistical analysis was performed using Student's t-test. RESULTS: Serial sections were required to confirm diagnosis in four of 52 cases. Thermal artifacts were present in all cases, with grade I being the most common (94.2% of cases). Clear margins were found in 96.2% of cases. No association was found between glandular involvement and CIN 1 (P > 0.05); however, there was an association with CIN 2 and CIN 3 (P < 0.05). CONCLUSION: The amount of excised tissue was sufficient, thermal artifacts were slight, resection margins were clear in most of cases, and a possible association was found between glandular involvement and the severity of the lesion.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".