Relationship of MRI tests and referral of malignant adnexal masses to gynecologic oncologists for surgery.
Bibliographic record
Abstract
6134 Background: To evaluate the patterns of radiologic imaging by family physicians and gynecologists in the work up of women found to have an adnexal mass on pelvic ultrasound. To evaluate whether advanced imaging tests are associated with improved referral of high risk adnexal masses to gynecologic oncologists. Methods: Centralized provincial databases of healthcare utilization were used to identify women aged 45 and older who received a pelvic ultrasound between 2006-2008. Subsequent imaging tests ordered by physician specialty were identified. Of those women who proceeded to laparotomy, logistic regression was performed to determine which imaging tests were associated with referral of high risk adnexal tumors to a gynecologic oncologist. Results: 193, 261 women had a pelvic ultrasound; 19, 949 (10.3%) had a laparotomy. 2223 and 627 women were categorized with benign and malignant adnexal masses respectively. Up to 12% of women had a pelvic MRI and 58% of women had a CT scan after a pelvic ultrasound.Family physicians referred 58% and gynecologists referred 47% of high risk ovarian masses to a gynecologic oncologist respectively after imaging.Gynecologic Oncologists operated on only 55% of women with malignant adnexal masses. On multivariate analysis factors significant for surgery by a gynecologic oncologist include a preoperative CT Scan OR 3.58 (p<.001) and CT Scan and MRI OR 7.78 (p<.001). Preoperative MRI alone had an OR of 1.86 (p=0.09) and was not significant. Mean time to surgery significantly increased when further imaging tests were performed after a pelvic ultrasound (100 days), CT (131 days), MRI (170 days), CT and MRI (179 days),P 0.002. Conclusions: The addition of a pelvic MRI to a pelvic ultrasound does not improve the referral of high risk adnexal masses to a gynecologic oncologist. A consensus on appropriate imaging and triage is needed when an adnexal mass is identified on ultrasound.
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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 | 0.000 | 0.010 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".