Oxytocin and Oxytocinase in the Obese and Nonobese Parturients during Induction and Augmentation of Labor
Bibliographic record
Abstract
Objective To investigate differences in oxytocin (OXT) biodistribution between nonobese and obese parturients during labor. Study Design Patients with body mass index (BMI) of either ≥ 18 ≤ 24.9 kg/m2 (“nonobese”) or ≥ 30 kg/m2 (“obese”) undergoing elective induction of labor were included (N = 25 each). Blood samples were collected at baseline (T0), and 20 minutes after maximal OXT augmentation or adequate uterine contractions (T1) for OXT and oxytocinase assays. A mixed-model repeated-measures analysis of variance was used to test for group versus time interaction and analysis of covariance to detect a difference in OXT level at T1. Data presented as mean ± standard deviation or median (interquartile range), with p < 0.05 considered significant. Results The mean BMIs (kg/m2) were 22.1 ± 1.6 and 35.9 ± 5.1 in the nonobese and obese groups, respectively. No differences were observed in either the duration of OXT infusion, total dose of OXT, or plasma OXT (pg/mL) either at T0 or T1. However, plasma oxytocinase (ng/mL) was significantly lower at T0 (1.41 [0.67, 3.51] vs. 0.40 [0.29, 1.12]; p = 0.03) in the obese group. Conclusion We provide preliminary evidence that the disposition of OXT may not be different between obese and nonobese women during labor.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".