Diabetes in pregnancy outcomes: A systematic review and proposed codification of definitions
Bibliographic record
Abstract
Rising rates of diabetes in pregnancy have led to an escalation in research in this area. As in any area of clinical research, definitions of outcomes vary from study to study, making it difficult to compare research findings and draw conclusions. Our aim was to compile and create a repository of definitions, which could then be used universally. A systematic review of the literature was performed on published and ongoing randomized controlled trials in the area of diabetes in pregnancy between 01 Jan 2000 and 01 Jun 2012. Other sources included the World Health Organization and Academic Society Statements. The advice of experts was sought when appropriate definitions were lacking. Among the published randomized controlled trials on diabetes and pregnancy, 171 abstracts were retrieved, 64 full texts were reviewed and 53 were included. Among the ongoing randomized controlled trials published in ClinicalTrials.gov, 90 protocols were retrieved and 25 were finally included. The definitions from these were assembled and the final maternal definitions and foetal definitions were agreed upon by consensus. It is our hope that the definitions we have provided (i) will be widely used in the reporting of future studies in the area of diabetes in pregnancy, that they will (ii) facilitate future systematic reviews and formal meta analyses and (iii) ultimately improve outcomes for mothers and babies.
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 | 0.112 | 0.229 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.017 |
| Bibliometrics | 0.042 | 0.034 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.014 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".