Locally advanced cervical cancer and para-aortic lymphadenectomy: impact of the number of removed lymph nodes, a FRANCOGYN group study
Bibliographic record
Abstract
INTRODUCTION: Cervical cancer is the fourth most common cancer worldwide among women. Surgical staging by para-aortic lymph node dissection (PALND) is performed when the cancer is locally advanced (LACC). There are no recommendations concerning the number of lymph node that must be removed during this surgery which hasn't prove is effectiveness concerning survival. PATIENTS AND METHODS: We conducted a retrospective multicenter descriptive and comparative study with data from FRANCOGYN group. We included 578 patients with LACC (IB3-IVA FIGO 2028) who underwent a PALND, 190 with <10 nodes and 388 with at least 10 nodes. The primary outcome was to evaluate the impact of the number of lymph nodes removed on the positivity of the staging. The secondary outcomes were to evaluate the impact of the number of lymph nodes removed on the treatment, the morbidity and the survival of the patients. RESULTS: There was no significant difference concerning the positivity of the staging between the two groups with 17,4 % and 16,2 % of positive staging (p = 0,8). There were no significant differences concerning the peri and post operative complications, the modification of the stage and treatment or the OS and DFS. CONCLUSION: It would appear that para-aortic staging with at least 10 or more nodes does not confer any advantage in terms of positivity and survival over staging with fewer than 10 nodes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".