MOESM1 of The impact of diagnostic criteria for gestational diabetes on its prevalence: a systematic review and meta-analysis
Bibliographic record
Abstract
Additional file 1. Table S1. Quality assessment of studies included using the Newcastleâ Ottawa Quality Assessment Scale for cohort studies. Table S2. Quality assessment of included studies using the Newcastleâ Ottawa Quality Assessment Scale for cross-sectional study. Figure S1. Flow chart of the literature search for the systematic review and meta-analysis. Figure S2. Bubble plot of Prevalence GDM vs. GDM diagnostic criteria*. Figure S3. Forest plot of Pooled Prevalence for region A in subgroup of GDM diagnostic criteria. Figure S4. Forest plot of Pooled Prevalence for region B in subgroup of GDM diagnostic criteria. Figure S5. Forest plot of Pooled Prevalence for region C in subgroup of GDM diagnostic criteria. Figure S6. Forest plot of Pooled Prevalence for region D in subgroup of GDM diagnostic criteria. Figure S7. Forest plot of Pooled Prevalence for region E in subgroup of GDM diagnostic criteria. Figure S8. Risk of bias in cross-sectional studies. Figure S9. Risk of bias in cohort studies.
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 | 0.015 | 0.101 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.018 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.403 | 0.010 |
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".