EPV054/#283 Does surgical approach influence recurrence in early stage cervical cancer with no gross visible disease at presentation?
Bibliographic record
Abstract
Objectives Published results from the LACC trial reported inferior survivals after minimal invasive surgical (MIS) approach in the treatment of early cervical cancer. Spillage of gross tumours and peritoneal contamination had been proposed as possible explanations. We studied oncologic outcomes specifically in patients presenting with no clinical gross cervical cancer treated with minimal invasive versus open radical hysterectomy as this has not been reported. Methods Retrospective chart reviews of all patients treated with radical surgery for cervical cancer from 2005 to 2018 were performed. Only patients with no gross visible tumour who were diagnosed after a LEEP/cone biopsies were included. Relevant demographics, pathologies and survival outcomes were abstracted. Descriptive and Chi Square statistics were used to summarize clinical variables. Kaplan Meier and Cox regression were used to study survival outcomes. All p<0.05 were considered to be statistically significant. Results 98 patients were included. Median age was 42. Median tumour size was 10 mm. Most was diagnosed after a cone biopsy (66%). Stage 1B1 was documented in 66% preoperatively. MIS used in 20 patients. Uterine manipulator used in 14 cases. Median follow up was 42 months. One recurrence in MIS group (5%) vs six recurrence in laparotomy group (7.7%), p=0.67.Three death in laparotomy and no death in MIS cohort. MIS is not significant in Cox model for PFS, adjusted for use of adjuvant radiation, and tumour size, p=0.43. Conclusions MIS radical hysterectomy might be safe in patients with no gross visible tumour at presentation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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".