The influence of intraoperative tumor rupture on recurrence risk in Stage Ic epithelial ovarian cancer.
Bibliographic record
Abstract
OBJECTIVE: To characterize the outcomes of patients with Stage Ic epithelial ovarian carcinoma, taking into consideration the criteria that were used to assign staging. We hypothesized that tumor rupture is a less ominous prognosticator in early-stage epithelial ovarian cancer than malignant washings or ovarian surface invasion. METHODS: A retrospective analysis of patients diagnosed with Stage I epithelial ovarian carcinoma at the University of Minnesota between 1990 and 2005 was carried out. Information was collected about demographics, diagnosis date, stage, grade, adjuvant treatment, last contact date and status at last contact. Statistical analysis was performed using the Kaplan-Meier method and the Cox proportion hazard model. RESULTS: One hundred and seventeen patients with Stage I epithelial ovarian cancer were identified and included in this review. Three distinct groups of patients were considered: 1) patients with Stage Ic cancers, so-assigned because of intraoperative tumor rupture only, 2) patients with Stage Ic cancers, so-assigned for any other reason(s) than rupture alone, and 3) patients with Stages Ia and Ib cancers. The recurrence risk of patients in group 1 was not significantly different from that of patients in groups 2 or 3 (p values 0.13 and 0.69, respectively), although a trend toward decreased risk of recurrence was seen in patients from group 1 compared to both other groups. CONCLUSIONS: In our cohort of patients, the risk of tumor recurrence in patients with Stage Ic epithelial ovarian cancer, so-assigned because of intraoperative rupture alone, is not significantly different from the two other groups of patients with Stage I disease.
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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 | 0.006 |
| 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".