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Record W2947957959 · doi:10.1055/s-0039-1692196

Oxytocin and Oxytocinase in the Obese and Nonobese Parturients during Induction and Augmentation of Labor

2019· article· en· W2947957959 on OpenAlexaff
Annemaria De Tina, Jeremy Juang, Thomas F. McElrath, Jack Baty, Arvind Palanisamy

Bibliographic record

VenueAmerican Journal of Perinatology Reports · 2019
Typearticle
Languageen
FieldPsychology
TopicNeuroendocrine regulation and behavior
Canadian institutionsMcMaster University
FundersSociety for Obstetric Anesthesia and Perinatology
KeywordsMedicineOxytocinInterquartile rangeAnalysis of varianceBody mass indexInternal medicineEndocrinologyLabor inductionPregnancy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score0.262

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.301
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2019
Admission routes1
Has abstractyes

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