Clinical Characteristics Differentiating Uterine Sarcoma and Fibroids
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Uterine fibroids are a common indication for laparoscopy. Unsuspected sarcoma can pose a serious risk if morcellation is used in the procedure. We sought to determine the clinical factors associated with uterine sarcoma compared with uterine fibroids. METHODS: We conducted a case-control study of 66 women who had hysterectomy for uterine sarcoma from April 1, 2007, to March 31, 2014. Sixty-six patients who had hysterectomy for fibroids were randomly selected as controls. RESULTS: = .02). CONCLUSION: Despite limitations in sample size related to infrequency of uterine sarcoma, our results suggest some preoperative clinical differences between women who have uterine sarcoma vs uterine fibroids. Further studies on such features may assist us in identifying patients who are at higher risk of having a uterine sarcoma among women with a uterine mass contemplating surgery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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 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".