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Different intensities of glycaemic control for women with gestational diabetes mellitus

2016· review· en· W1593296516 on OpenAlexaboutno aff
Ruth Martis, Julie Brown, Jane M. Alsweiler, Tineke J Crawford, Caroline A Crowther

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2016
Typereview
Languageen
FieldMedicine
TopicGestational Diabetes Research and Management
Canadian institutionsnot available
FundersNational Health and Medical Research CouncilEvidence Synthesis ProgrammeNational Institute for Health and Care Research
KeywordsMedicineGestational diabetesPopulationChildbirthPregnancyObstetricsDiabetes mellitusRandomized controlled trialPediatricsGestationInternal medicineEndocrinology

Abstract

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BACKGROUND: Gestational diabetes mellitus (GDM) has major short- and long-term implications for both the mother and her baby. GDM is defined as a carbohydrate intolerance resulting in hyperglycaemia or any degree of glucose intolerance with onset or first recognition during pregnancy from 24 weeks' gestation onwards and which resolves following the birth of the baby. Rates for GDM can be as high as 25% depending on the population and diagnostic criteria used and rates are increasing globally. Risk factors associated with GDM include advanced maternal age, obesity, ethnicity, family history of diabetes, and a previous history of GDM, macrosomia or unexplained stillbirth. There is wide variation internationally in glycaemic treatment target recommendations for women with GDM that are based on consensus rather than high-quality trials. OBJECTIVES: To assess the effect of different intensities of glycaemic control in pregnant women with GDM on maternal and infant health outcomes. SEARCH METHODS: We searched the Cochrane Pregancy and Childbirth Group's Trials Register (31 January 2016), ClinicalTrials.gov, the WHO International Clinical Trials Registry Platform (ICTRP) (1 February 2016) and reference lists of the retrieved studies. SELECTION CRITERIA: We included one randomised controlled trial. Cluster-randomised and quasi-randomised controlled trials were eligible for inclusion. DATA COLLECTION AND ANALYSIS: We used the methods described in the Cochrane Handbook for Systematic Reviews of Interventions for carrying out data collection, assessing study quality and analysing results. Two review authors independently assessed trial eligibility for inclusion, evaluated methodological quality and extracted data for the one included study. We sought additional information from one trial author but had no response. We assessed the quality of evidence for selected outcomes using the GRADE approach. MAIN RESULTS: We included one Canadian trial of 180 women, recruited between 20 to 32 weeks' gestation, who had been diagnosed with GDM. Data from 171 of the 180 women were published as a conference abstract and no full report has been identified. The overall risk of bias of the single included study was judged to be unclear.The included trial did not report on any of this review's primary outcomes. For the mother, these were hypertension disorders of pregnancy or subsequent development of type 2 diabetes. For the infant, our primary outcomes were (perinatal (fetal and neonatal) mortality; large-for-gestational age; composite of death or severe morbidity or later childhood neurosensory disability).The trial did report data relating to some of this review's secondary outcomes. There was no clear difference in caesarean section rates for women assigned to using strict glycaemic targets (pre-prandial 5.0 mmol/L (90 mg/L) and at one-hour postprandial 6.7 mmol/L (120 mg/dL)) (28/85, 33%) when compared with women assigned to using liberal glycaemic targets (pre-prandial 5.8 mmol/L (103 mg/dL) and at one-hour postprandial 7.8 mmol/L (140 mg/dL)) (21/86, 24%) (risk ratio (RR) 1.35, 95% confidence interval (CI) 0.83 to 2.18, one trial, 171 women; very low quality). Using the GRADE approach, we found the quality of the evidence to bevery low for caesarean section due to poor reporting of risk of bias, imprecision and publication bias. Strict glycaemic targets were associated with an increase in the use of pharmacological therapy (identified as the use of insulin in this study) (33/85; 39%) compared with liberal glycaemic targets (18/86; 21%) (RR 1.85, 95% CI 1.14 to 3.03; one trial, 171 women). CIs are wide suggesting imprecision and caution is required when interpreting the data. No other secondary maternal outcome data relevant to this review were reported. For the infant, there were no clear differences between the groups of women receiving strict and liberal glycaemic targets for macrosomia (birthweight greater than 4000 g) (RR 1.35, 95% CI 0.31 to 5.85, one trial, 171 babies); small-for-gestational age (RR 1.12, 95% CI 0.48 to 2.63, one trial, 171 babies); birthweight (mean difference (MD) -92.00 g, 95% CI -241.97 to 57.97, one trial, 171 babies) or gestational age (MD -0.30 weeks, 95% CI -0.73 to 0.13, one trial, 171 babies). Adverse effects data were not reported. No other secondary neonatal outcomes relevant to this review were reported. AUTHORS' CONCLUSIONS: This review is based on a single study (involving 180 women) with an unclear risk of bias. The trial (which was only reported in a conference abstract) did not provide data for any of this review's primary outcomes but did provide data for a limited number of our secondary outcomes. There is insufficient evidence to guide clinical practice for targets for glycaemic control for women with GDM to minimise adverse effects on maternal and fetal health. Glycaemic target recommendations from international professional organisations for maternal glycaemic control vary widely and are reliant on consensus given the lack of high-quality evidence.Further high-quality trials are needed, and these should compare different glycaemic targets for guiding treatment of women with GDM, assess both short-term and long-term health outcomes for women and their babies, include women's experiences and assess health services costs. Four studies are ongoing.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.092
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.001
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 head0.068
GPT teacher head0.359
Teacher spread0.291 · 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.

Study designSystematic review
Domainnot available
GenreReview

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".

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Citations40
Published2016
Admission routes1
Has abstractyes

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