Evaluation of Surgical Approaches and Use of Adjuvant Radiotherapy with Respect to Oncologic Outcomes in the Management of Clinically Early-Stage Cervical Carcinoma
Bibliographic record
Abstract
The standard of care for early-stage cervix cancer is radical hysterectomy with pelvic lymphadenectomy. Adjuvant radiotherapy (RT) or chemoradiotherapy may be administered to reduce the risk of recurrence in patients considered to be at elevated risk based on a combination of pathologic factors. We performed a retrospective review to determine oncologic outcomes in patients treated for early-stage cervix cancer and to determine if surgical approach impacted oncologic outcomes or the decision to use adjuvant therapy. In total, 174 women underwent radical hysterectomy and pelvic lymphadenectomy over the 15-year period. Most of these women (146) had open surgery and 28 had minimally invasive surgery (MIS). In total, 81 had adjuvant pelvic RT; 76 in the open surgery group (52%) and 5 in the MIS group (18%). Five-year PFS and OS, respectively, were 84% and 91%. Five-year PFS was significantly lower in patients who had MIS vs. open surgery, without a difference in 5-year OS, suggesting MIS should be avoided. Five-year PFS was the same with RT or with its omission, despite those treated with RT having higher risk disease. We have demonstrated excellent outcomes in patients with early-stage cervix cancer after primary surgery and selective use of RT, with few recurrences and excellent survival.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".