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Record W2964106550 · doi:10.1186/s12884-019-2413-5

Seasonality, temperature and pregnancy oral glucose tolerance test results in Australia

2019· article· en· W2964106550 on OpenAlexaff
E. Shen, Robert G. Moses, Jeremy Oats, Julia Lowe, David McIntyre

Bibliographic record

VenueBMC Pregnancy and Childbirth · 2019
Typearticle
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersSociety of Obstetric Medicine of Australia and New Zealand
KeywordsMedicineGestational diabetesPlasma glucosePregnancyDiabetes mellitusEndocrinologyInternal medicineGestationGlucose tolerance testPost-hoc analysisObstetricsAnimal scienceInsulin resistanceBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The oral glucose-tolerance test (OGTT) is currently the standard method for diagnosis of gestational diabetes (GDM). We conducted a post hoc analysis using the Australian Hyperglycemia and Adverse Pregnancy Outcome (HAPO) data to determine seasonal variations in OGTT results, the consequent prevalence of GDM, and association with select perinatal parameters. METHOD: Women enrolled in the Australian HAPO study sites (Brisbane and Newcastle) from 2001 to 2006 were included if OGTT results between 24 to 32 weeks gestation were available (n = 2120). Fasting plasma glucose, 1-h plasma glucose, 2-h plasma glucose, HbA1c, HOMA-IR, and umbilical cord C-peptide and glucose values were categorized by season and correlated to monthly temperature records from the Australian Bureau of Meteorology for Brisbane and Newcastle. GDM was defined post hoc using the IADPSG/WHO criteria. RESULTS: Small but significant (p < 0.01 on ANOVA) elevations in fasting glucose (+ 0.12 mM), HbA1c (+ 0.09%), and HOMA-IR (+ 0.88 units) were observed during the winter months. Conversely, higher 1-h (+ 0.19 mM) and 2-h (+ 0.33 mM) post-load glucose values (both p < 0.01) were observed during the summer months. The correlations between fasting glucose, 1-h glucose, 2-h glucose, and HbA1c with average monthly temperatures confirmed this trend, with positive Pearson's correlations between 1-h and 2-h glucose with increasing average monthly temperatures, and negative correlations with fasting glucose and HbA1c. Further, umbilical cord C-peptide and glucose displayed negative Pearson's correlation with average monthly temperature, aligned with trends seen in the fasting plasma glucose. Overall prevalence of GDM did not display significant seasonal variations due to the opposing trends seen in the fasting versus 1-h and 2-h post-load values. CONCLUSION: A significant winter increase was observed for fasting plasma glucose, HbA1c, and HOMA-IR, which contrasted with changes in 1-h and 2-h post-load venous plasma glucose values. Interestingly, umbilical cord C-peptide and glucose displayed similar trends to that of the fasting plasma glucose. While overall prevalence of GDM did not vary significantly by seasons, this study illustrates that seasonality is indeed an additional factor when interpreting OGTT results for the diagnosis of GDM and provides new direction for future research into the seasonal adjustment of OGTT results.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.047
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.290
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2019
Admission routes1
Has abstractyes

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