Bibliographic record
Abstract
The indications for magnetic resonance (MR) imaging of the female pelvis have expanded considerably over the past decade. The impetus behind these expanding indications is multifactorial. First, there has been widespread dissemination of MR hardware and software techniques that allow the routine acquisition of images with high spatial or temporal resolution. Second, the advent of minimally invasive therapies in the management of gynecologic disorders has created a need for increased accuracy in the preoperative evaluation of these patients. Finally, in this era of cost containment, several studies have shown that the appropriate use of MR imaging in the diagnostic algorithm minimizes cost. In this issue of Topics in Magnetic Resonance Imaging, Dr. Troiano reviews the classification of mullerian duct anomalies and associated MR imaging findings. In addition, he dispels a number of myths that radiologists hold regarding classification of these anomalies. Dr. Ascher and colleagues present us with an example of a new indication for an old disease, by providing the reader with an in-depth discussion on the role of MR imaging in evaluating patients with leiomyomas referred for uterine artery embolization. Dr. Sala and Dr. Atri provide us with clear guidelines for the use of MR imaging in evaluating patients with an adnexal mass at transvaginal sonography. Although MR imaging often can be “tissue specific,” failing that, MR is highly accurate at differentiating nonsurgical from surgical lesions, and in the case of surgical lesions, suggesting the appropriate surgical approach, i.e., laparoscopy versus the more invasive laparotomy. In patients with ovarian malignancy, Dr. Funt and Dr. Hricak outline the utility of cross-sectional imaging prior to primary and secondary cytoreductive surgery. Finally, Dr. Chaudhry and colleagues review the role of MR imaging as well as the common imaging findings in the evaluation of benign and malignant disease of the endometrium. It has been a tremendous pleasure collaborating with this international group of experts. It is my hope their efforts will take us one step closer to establishing MR imaging as a critical step in the evaluation and management of patients with suspected female pelvic pathology. I am indebted to all authors for their extraordinary contributions of time and expertise, to Scott Atlas for his invitation to compile these papers, and to Andrea Allison-Williams for her endless patience and invaluable editorial assistance.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".