Recommendation Patterns Among Obstetrician–Gynecologists and Radiologists for Complex Adnexal Masses on Ultrasonography [370]
Bibliographic record
Abstract
INTRODUCTION: The follow-up recommendations of newly identified adnexal masses on ultrasound evaluation remain controversial among gynecologists and radiologists. The objective of this study is to compare patterns of recommendations for new adnexal masses described on ultrasonography based on the interpreter field of specialty. METHODS: In the McGill University Hospital Network, there are two hospitals that differ in the specialty department that reports gynecologic ultrasonographies: one has the ultrasonograms reported exclusively by gynecologists and the other exclusively by radiologists. We carried out a review of all pelvic ultrasonograms conducted at these two sites between May and June 2014 on all newly identified adnexal masses in nonpregnant women. Masses were classified by reported features, diagnosis, and management recommendations. χ2 analyses were used to compare recommendations among specialty fields. RESULTS: Of the 1,111 reports reviewed, 201 were eligible, among which 69 (34%) were reported by gynecologists and 132 (66%) by radiologists. Complex masses were reported by gynecologists in 23 (33.3%) studies and in 54 (40.9%) studies by radiologists. Reported adnexal mass types were not significantly different between the two sites (P=.26). Among complex masses, gynecologists were less likely than radiologists to recommended follow-up ultrasonography (13.0% compared with 40.7%, P<.05), recommend computed tomography or magnetic resonance imaging (4.4% compared with 24.1%, P<.05), but more likely to commit to a strong suspicion of malignancy (17.4% compared with 3.7%, P<.05, respectively). CONCLUSION: There are significant differences in recommendation patterns between gynecologists and radiologists evaluating new adnexal masses on ultrasonography. This difference can have important effects on resource use and patient concerns.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".