ACR Appropriateness Criteria® Infertility
Bibliographic record
Abstract
Appropriate imaging for women undergoing infertility workup depends upon the clinician's suspicion for potential causes of infertility. Transvaginal US is the preferred modality to assess the ovaries for features of polycystic ovary syndrome (PCOS), the leading cause of anovulatory infertility. For women who have a history or clinical suspicion of endometriosis, which affects at least one third of women with infertility, both MRI and pelvic US can provide valuable information. If tubal occlusion is suspected, whether due to endometriosis, previous pelvic inflammatory disease, or other cause, hysterosalpingogram (HSG) is the preferred method of evaluation. To assess for anatomic causes of recurrent pregnancy loss (RPL) such as Müllerian anomalies, synechiae, and leiomyomas, saline infusion sonohysterography, MRI and 3-D US are most appropriate. Up to 10% of women suffering recurrent pregnancy loss have a congenital Müllerian anomaly. When assessment of the pituitary gland is indicated, MRI is the imaging exam of choice.The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed every three 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 | 0.005 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.016 |
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".