Ultrasound evaluation of first trimester pregnancy complications
Bibliographic record
Abstract
OBJECTIVES: First, to review normal embryonic development and the sonographic evidence of early pregnancy failure; second, to review sonographic evidence of ectopic pregnancy. OUTCOMES: First, prediction of pregnancy failure, second, sonographic identification of ectopic pregnancy. EVIDENCE: A MEDLINE search and review of bibliographies in identified articles was conducted. VALUES: The evidence was reviewed by the Diagnostic Imaging Committee along with the principal authors. A quality of evidence assessment was undertaken as outlined in the report of the Canadian Task Force on the Periodic Health Examination (Table 1). BENEFITS, HARMS, AND COSTS: Women presenting with first trimester bleeding may be incorrectly diagnosed with a missed abortion and (or) may be inappropriately reassured about viability. Transvaginal ultrasound provides improved resolution allowing description of early embryonic development characteristics. Improvement in the identification of the sonographic landmark of normal embryonic development and awareness of the sonographic risk factors of pregnancy failure may lead to more successful management strategies. Diagnosis of suspected ectopic pregnancy often involves an assessment of both hormonal markers and sonographic features. Maternal morbidity and mortality can be reduced with an early diagnosis of ectopic pregnancy. RECOMMENDATIONS: There is good (class A) evidence that current ultrasound technology can distinguish between normal and abnormal pregnancies in the first trimester. There is good (class A) evidence that transvaginal ultrasound in conjunction with quantitative-HCG can diagnose ectopic 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".