ACR Appropriateness Criteria® Pretreatment Evaluation and Follow-Up of Endometrial Cancer
Bibliographic record
Abstract
Endometrial cancer is the most common gynecologic and the fourth most common malignancy in women in the United States. Cross-sectional imaging plays a vital role in pretreatment assessment of endometrial cancers and should be viewed as a complementary tool for surgical evaluation and planning of these patients. Although transvaginal US remains the preferred examination for the screening purposes, MRI has emerged as the modality of choice for the staging of endometrial cancer and imaging assessment of recurrence or treatment response. A combination of dynamic contrast-enhanced and diffusion weighted MRI provides the highest accuracy for the staging. Both CT and MRI perform equivalently for assessing nodal involvement or distant metastasis. PET-CT is more appropriate for assessing lymphadenopathy in high-grade FDG-avid tumors or for clinically suspected recurrence after treatment. An appropriate use and guidelines of imaging techniques in diagnosis, staging, and detection of endometrial cancer and treatment of recurrent disease are reviewed.The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every two years by a multidisciplinary expert panel. The guideline development and review include an extensive analysis of current medical literature from peer reviewed journals and the application of a well-established consensus methodology (modified Delphi) to rate the appropriateness of imaging and treatment procedures by the panel. In those instances where evidence is lacking or not definitive, expert opinion may be used to recommend imaging or treatment.
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 | 0.008 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".