Bibliographic record
Abstract
Role of imaging The role of imaging is to confirm the diagnosis of uterine leiomyoma and to differentiate leiomyomas from other causes of uterine enlargement or pelvic masses such as ovarian or endometrial based masses, adenomyosis, serosal implants and lymphadenopathy. In addition, the number, size, and location of leiomyomas must be assessed. This is particularly important in the symptomatic, infertile, or pregnant patient. Possible complications including benign degeneration should be recognized. Signs suggestive of malignant transformation must be evaluated. Imaging is useful in preoperative mapping, particularly in the setting of uterus-sparing procedures and for therapy monitoring. General histology Uterine leiomyomas are well-circumscribed, benign smooth muscle neoplasms with various amounts of fibrous connective tissue. Leiomyomas may be single or, more frequently, multiple. Uterine leiomyomata are estrogen-sensitive neoplasms that occur in 20–30% of reproductive-aged women. Leiomyomas regress during anovulatory cycles as a result of unopposed estrogen stimulation. As leiomyomas enlarge, they may outgrow their blood supply, resulting in ischemia and degeneration characterized as hyaline, cystic, myxomatous, fatty, or hemorrhagic. Rapid increase in size of leiomyomas in a postmenopausal patient should raise the possibility of sarcomatous change. Classification by location Leiomyomas originate from the uterine corpus in the vast majority of cases; however, rarely (3–8%) they can arise from the cervical region. Uterine leiomyomas are categorized with respect to their location (subserosal, intramural, submucosal).
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".