Bibliographic record
Abstract
OBJECTIVE: To determine the best Doppler values for differentiating ectopic pregnancy from a corpus luteum cyst of pregnancy. METHODS: This was a prospective study of 80 consecutive patients with the diagnosis of ectopic pregnancy. All ectopic pregnancies were diagnosed on the basis of the presence of an extra-ovarian adnexal mass on sonography and were confirmed surgically. The last menstrual period ranged from 4 to 11 weeks (mean, 6.3 weeks), and the maximal ectopic pregnancy diameter ranged from 0.7 to 5.5 cm (mean, 2.5 cm). Seventy-six ectopic pregnancies showed color vascularity, and 40 showed corpus luteum cysts with vascular walls. The highest peak systolic velocity and the lowest resistive index of the vascular ectopic pregnancies were compared with the corresponding values in the vascular corpus luteum cysts. RESULTS: The mean peak systolic velocity of the ectopic pregnancies was 35.4 cm/s compared with 28.4 cm/s in corpus luteum cysts, with no significant statistical difference (P = .1). The resistive index of the ectopic pregnancies ranged from 0.15 to 1.6 (mean +/- SD, 0.61 +/- 0.24) compared with 0.39 to 0.7 (mean, 0.52 +/- 0.10) in corpus luteum cysts, with a significant statistical difference (P = .003). In this cohort, a resistive index of less than 0.39 had a specificity of 100% and a positive predictive value of 100% for diagnosing ectopic pregnancy but was present in only 15% (confidence interval, 7%-23%) of ectopic pregnancies. A resistive index of greater than 0.7 had a specificity of 100% and a positive predictive value of 100% for diagnosing ectopic pregnancy and was present in 31% (confidence interval, 21%-41%) of ectopic pregnancies. CONCLUSIONS: Both low and high resistive indices discriminate ectopic pregnancy from a corpus luteum cyst.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".