Benign Myometrial Conditions: Leiomyomas and Adenomyosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it