Variation in outcome reporting in studies on obesity in pregnancy—A systematic review
Bibliographic record
Abstract
Although considerable research is being conducted with a view to improve outcomes for pregnant women with obesity and their babies, much of this research is compromised by the quality of outcome reporting. Our aim is to determine how outcomes have been reported and measured in obesity in pregnancy studies, as a first step towards developing a core outcome set to standardize outcome reporting in future trials. We conducted a systematic review of clinical trials and systematic reviews on obesity in pregnancy in accordance with the Preferred Reporting in Systematic Reviews and Meta-analyses guidelines. We searched Medline, Embase, controlled register of trials, World Health Organization International Clinical Trials Registry, www.clinicaltrials.gov and Google Scholar, for relevant studies and extracted study characteristics, outcome reporting and measurement. Reporting quality was assessed using previously published criteria. Outcomes were grouped using a published taxonomy and variations in outcome reporting and measurement were descriptively presented. Seventy included studies yielded a total of 135 outcomes. Foetal/neonatal outcomes were not reported in 53.3% of studies where an intervention could have implications to both, mother and baby. Reported outcomes were mostly physiological/clinical (74.8%), with very limited representation of outcomes related to mortality/survival (5.2%), life impact (7.4%), adverse events (5.9%) and resource utilization (6.7%).
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 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 | 0.234 | 0.552 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.020 | 0.027 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".