Variation in Outcome Reporting in Studies on Obesity in Pregnancy—A Systematic Review [20S]
Bibliographic record
Abstract
INTRODUCTION: Improving outcomes in women with obesity (BMI>30 kg/m2) in pregnancy is limited by the heterogeneous outcomes between clinical trials. This systematic review of antepartum and peripartum interventions/exposures aimed to determine outcomes and their definitions reported thus far, as a preliminary step towards the standardization of outcomes. METHODS: MEDLINE, Embase, CENTRAL and www.clinicalTrials.gov were searched for trials, secondary analyses and systematic reviews in English between 01 January 2000 and 20 November 2017. Grey literature and reference lists of included studies were searched. Title/abstracts and full-texts were screened in duplicate. The following data were extracted: study characteristics, primary, secondary maternal, fetal and neonatal outcomes (including composites), their definitions or measurements and outcome reporting quality (MOMENT criteria). The proportion of studies reporting each outcome, a primary outcome and variations in definitions and components of composite outcomes were determined. RESULTS: Of 4916 results screened, 218 full-texts were assessed and 70 included. 173 maternal/obstetric outcomes and 86 neonatal/fetal outcomes were reported. 49 of the 58 trials/registrations reported a primary outcome, 21 of which were not in a reproducible way. The most frequent maternal outcome was gestational weight gain (24 studies) with inconsistent definitions between studies. The most frequent fetal/neonatal outcome was birthweight (17 studies). There were three maternal composite outcomes, one fetal/neonatal and one combined. No maternal composite comprised the same components. CONCLUSION: The high number of outcomes prevents the reporting of relevant outcomes in all obesity in pregnancy studies. Further, inconsistencies and variations between outcome choices and definitions identified present challenges for study comparability and data aggregation.
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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 | 0.215 | 0.505 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.019 | 0.030 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".