COMPARISON OF FETAL BEHAVIOR IN LOW- AND HIGH-RISK PREGNANCIES
Bibliographic record
Abstract
Meta-analyses were conducted on archival data of human fetal behavior to identify differential behavior among high-risk fetuses in pregnancies complicated by threatened preterm delivery, maternal hypertension or diabetes compared with low-risk fetuses in uneventful pregnancies, delivering as healthy, full-term infants. Data for a total of 493 fetuses (260 high risk, 233 low risk) from 23 weeks' gestation to term who participated in a study using a standardized protocol including observations of spontaneous and auditory-induced behavior were retrieved from our laboratory database. There were no differences in spontaneous behaviors when scored using clinical criteria for the nonstress test and biophysical profile; however, there were differences in the magnitude of the behaviors measured in the tests. Developmental differences were observed between those threatening to deliver early and the fetuses of hypertensive and diabetic mothers. The latter two groups differed little from one another but differed from low-risk fetuses in their response to auditory stimulation. We concluded that differences in behavior among high-risk groups suggest that atypical fetal behaviors may represent adaptation to condition specific insult rather than a generalized response to insult per se. The finding that high-risk fetuses showed atypical responses to auditory stimuli indicates a need to examine the relation between fetal auditory function and later language acquisition.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| 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) | 0.001 | 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".