Bibliographic record
Abstract
Ultrasound is a valuable diagnostic tool, which can be used to stratify pregnant women with adnexal masses into a conservative management protocol versus those that require further diagnostic and management decisions. Familiarity with the natural history and sonographic features of common adnexal lesions, such as simple cysts, hemorrhagic cysts, endometriomas, mature cystic teratomas, and ovarian conditions specific to pregnancy, may permit stratification of patients into management protocols. The goal of ultrasound evaluation in the pregnant patient with an adnexal mass is to identify those patients in whom conservative management is appropriate versus those who require more immediate interventions such as surgery. The risk of surgical interventions needs to be balanced against the potential risks of nonintervention, which may include torsion, rupture, hemorrhage, or the rare spread of a malignant cancer. Atypical features or persistent large lesions should initiate a multidisciplinary team approach to optimize diagnostic and management strategy. Acute symptoms may precipitate emergency intervention at any point in the pregnancy. We will present a diagnostic and management algorithm based on clinical symptoms, timing of detection, natural history, and sonographic features of adnexal masses in pregnancy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 0.001 |
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".