Diagnostic et prise en charge des néoplasies intraépithéliales vulvaires et des cancers invasifs de la vulve
Bibliographic record
Abstract
OBJECTIVE: Update on the diagnosis and management of vulvar intraepithelial neoplasia (VIN) and vulvar carcinoma. METHOD: Review of the literature. RESULTS: The classification of VIN is leaning towards the development of two distinct anatomoclinical models. VIN of the usual type and of a differentiated type. New forms of treatment of preinvasive lesions that are less traumatic and as efficient will have to be evaluated in the future such as immunomodulators and photodynamic therapy. The invasive carcinoma is generally rapidly symptomatic and visible; however, more than one third of patients are still diagnosed at an advanced stage. New therapeutic alternatives are being examined such as the sentinel lymph node in early stages and the preoperative radiochemotherapy in advanced stages. CONCLUSIONS: VIN and vulvar cancer need specialized treatment which may be mutilating, despite the fact that therapeutic options are well defined today and tend towards less aggressive surgery.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".