Benign Myometrial Conditions: Leiomyomas and Adenomyosis
Bibliographic record
Abstract
Leiomyomas and adenomyosis are common benign myometrial conditions. Although their symptoms overlap, traditional treatment of these two entities differs; thus, making the correct diagnosis is critical. Specifically, uterine-conserving therapy is well established for many women with symptomatic leiomyomas, whereas hysterectomy is the treatment for debilitating adenomyosis. Magnetic resonance imaging (MRI) is the most accurate modality for identifying leiomyomas and adenomyosis. T2-weighted sequences often are diagnostic. For leiomyomas, MRI reliably identifies their number, size, and location. These features help triage patients to appropriate therapy. For adenomyosis, MRI establishes the diagnosis in cases of equivocal or nondiagnostic ultrasounds. MRI also has been used to confirm an ultrasound diagnosis of adenomyosis when curative surgery is being considered. Intravenous gadolinium chelates are not necessary to make the diagnosis of either adenomyosis or leiomyomas, but it provides useful information about vascularity of lesions, a factor that may impact the type of treatment undertaken.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".