OP02.01: Reliability of Doppler echocardiographic investigation in the diagnosis of fetuses with first degree atrioventricular block
Bibliographic record
Abstract
Oral poster abstractsfetuses and 1040 fetuses with Down syndrome were included in the analysis.Data Extraction: Articles were independently selected, reviewed, and abstracted by 2 reviewers.For each study, the positive likelihood ratio (LR), negative LR, sensitivity, and specificity were calculated.Overall estimates of pooled positive LR, negative LR, sensitivity, specificity, and pooled odds ratio with 95% confidence intervals were calculated for absent nasal bone.Data Synthesis: When the absence of nasal bone was observed in the first trimester, the odds of Down syndrome increased 48-fold (positive LR, 48; 95% CI 29-79).When the nasal bone was seen, the risk of a fetus having Down syndrome was halved (negative LR, 0.44; 95%CI 0.33-0.59).The pooled estimates of sensitivity for detecting fetuses with Down syndrome was 65% (95% CI 61%-69%) and specificity was 99% (95% CI 98%-99%).The diagnostic odds ratio for absent nasal bone was 112 (95% CI 66-187).Conclusions: The absence of nasal bone when observed in the first trimester is a useful tool in distinguishing Down syndrome fetuses.The overall sensitivity of absent nasal bone allows us to use it as a practical marker for Down syndrome.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".