Bibliographic record
Abstract
Renal cell carcinoma (RCC) diagnosis and management haveundergone significant shifts in the recent past. The increasing rateof diagnosis of small renal masses, often in patients at high risk ofmorbidity with operative treatment, has led to studies, trials anddiscoveries in renal mass biopsy, active surveillance and minimallyinvasive thermal ablation. At the other end of the disease spectrum,targeted systemic therapies for metastatic RCC have supplantedcytokine-based treatment, with significant benefits to progressionand survival. Recent reviews and trials have also cemented the roleof partial nephrectomy as standard surgical management for mostlow-stage masses, and the roles of regional lymphadenectomy andadrenalectomy concomitant with nephrectomy have been clarified.This review aims to highlight recent evidence that has emerged inthe management of this complicated oncologic issue.Le diagnostic d’hypernéphrome et la prise en charge de cettemaladie ont fait l’objet d’importants changements au cours desdernières années. Le taux accru de cas de petites masses rénales,souvent chez des patients présentant un risque élevé de morbiditéavec le traitement chirurgical, a amené la conduite d’étudeset d’essais qui ont entraîné des découvertes touchant la biopsiedes masses rénales, la surveillance active et l’ablation thermiqueminimalement invasive. À l’autre bout du spectre pathologique, lestraitements généraux ciblés de l’hypernéphrome métastatique ontsupplanté le traitement à base de cytokines, ce qui a amené desavantages significatifs sur le plan de la progression et de la survie.Des articles de synthèse et des essais récents ont aussi confirméle rôle de la néphrectomie partielle en tant que prise en chargechirurgicale standard pour la plupart des masses de faible stade,et les rôles de la lymphadénectomie régionale et de la surrénalectomieen concomitance avec une néphrectomie ont été clarifiés.Le présent article vise à faire ressortir les données récentes dans laprise en charge de ce problème oncologique complexe
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.006 |
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".