Limited-sequence magnetic resonance imaging in the evaluation of the ultrasonographically indeterminate pelvic mass.
Bibliographic record
Abstract
OBJECTIVE: To evaluate the usefulness of limited-sequence magnetic resonance imaging (MRI) in the elucidation of ultrasonographically indeterminate pelvic masses. This study focused only on pelvic masses in which the origin of the mass (uterine v. extrauterine) could not be determined by ultrasonography (US). The origin of a pelvic mass has clinical implications. A mass arising from the uterus is most likely to be a leiomyoma, which is a benign lesion, whereas an extrauterine mass will have a higher likelihood of malignancy and usually requires surgery. METHODS: Eighty-one female patients whose pelvic mass was of indeterminate origin on US also underwent limited-sequence MRI of the pelvis. Most of the MRI examinations were performed on the same day as the US. Limited-sequence MRI sequences included a quick gradient-echo T1-weighted localizer and a fast spin-echo T2-weighted sequence. Final diagnoses were established by surgical pathology or by clinical and imaging follow-up. RESULTS: Limited-sequence MRI was helpful in 79 of the 81 cases (98%). Fifty-two of the 81 masses (64%) were leiomyomas. One was a leiomyosarcoma. The extrauterine masses (26/81 [32%]) were identified as 14 ovarian malignancies, 4 endometriomas, 3 dermoids, an ovarian fibroma, an infarcted fibrothecoma, an infarcted hemorrhagic cyst, a sigmoid diverticular abscess and a gastrointestinal stromal tumour of the ileum. In the other 2 cases (2/81 [2%]), the origin of the pelvic mass remained indeterminate. Both of these indeterminate masses showed low signal on T2-weighted images and were interpreted as probable leiomyomas. They were not surgically removed but were followed clinically and had a stable course. CONCLUSION: Limited-sequence MRI is a quick and efficient way to further evaluate ultrasonographically indeterminate pelvic masses. Limited-sequence MRI of the pelvis can suffice, in these cases, without requiring a full MRI examination.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".