Histopathologic score predicts recurrence free survival after radical surgery in patients with stage IA<sub>2</sub>–IB<sub>1–2</sub> cervical carcinoma
Bibliographic record
Abstract
BACKGROUND: The authors evaluated clinical and pathologic factors that predicted for recurrence after patients underwent radical surgery for International Federation of Gynecology and Obstetrics (FIGO) Stage IA(2)-IB(1-2) cervical carcinoma and developed a simple method of scoring those predictive factors to quantify outcome. METHODS: An analysis was conducted of a prospective radical surgery cervical carcinoma data base. A Cox proportional hazards regression analysis was done for each of the individual factors to estimate individual risk ratios using all available data for each factor. Stepwise and best-model options were used to identify the best combinations as predictors and to calculate adjusted risk ratios. Based on the information obtained, each patient was assigned a categorical score to predict recurrence. The variables used for the score were dichotomized. The differences between the scores in time to recurrence were evaluated using the log-rank test to compare the time to recurrence curves that were generated with the Kaplan-Meier method. RESULTS: Eight hundred seventy-one patients were included in the study, and 66 patients who developed recurrent disease after a median follow-up of 49 months. Tumor size, maximum depth of invasion, pelvic lymph node status, tumor grade, and capillary lymphatic space (CLS) were single predictors for recurrence, and the score, which was based on combinations of these factors, predicted the disease free survival. Maximum depth of invasion, pelvic lymph node status, and CLS were the best combined predictors for recurrence, and they were used to form a second, precise scoring system to predict disease free survival (P < 0.0001; log-rank test). CONCLUSIONS: The scoring system based on maximal depth of invasion, CLS, and pelvic lymph node metastases identified four strata of patients with distinct recurrence free survival. The incremental presence of each factor decreased recurrence free survival after patients underwent radical surgery. Patients with the presence of all three factors had a 5-year recurrence free survival rate of 65%. These patients would be suitable for studies of postoperative adjuvant therapy to improve outcome.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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 teacher head, 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".