Additional file 1 of Intensive lactation among women with recent gestational diabetes significantly alters the early postpartum circulating lipid profile: the SWIFT study
Bibliographic record
Abstract
Additional file 1: Supplementary Figure S1-S7 and Table S1-S13. Figure S1. Quality control of the final metabolomics dataset at baseline and follow-up. Figure S2. Longitudinal analysis of metabolites between IBF and IFF/Mixed women from baseline to follow-up. Figure S3. Quality control of the final lipidomics dataset at baseline. Figure S4. Effects of postpartum lactation intensity on lipid profiling at baseline in IFG/IGT and NGT women. Figure S5. Effects of different lactation intensity on lipid profiling at early postpartum. Figure S6. Metabolites associated with extreme lactation intensity at baseline. Figure S7. Generation of the predictive models. Table S1. Differential analytes between IBF and IFF/Mixed women at baseline. Table S2. Differential analytes between IBF and IFF/Mixed women at follow-up. Table S3. Differential lipid species between IBF and IFF/Mixed women at baseline. Table S4. Relationship between lactation intensity and fatty acid composition in lipids. Table S5. Pathways associated with lactation intensity at baseline. Table S6. Potential genes associated with differentially expressed lipid species at baseline. Table S7. Differential lipid species between IBF and IFF/Mixed women in the no T2D subgroup. Table S8. Differential lipid species between IBF and IFF/Mixed women in the future T2D subgroup. Table S9. Differential analytes between IBF and IFF/Mixed women in the no T2D subgroup. Table S10. Differential analytes between IBF and IFF/Mixed women in the future T2D subgroup. Table S11. Baseline clinical characteristics of responders and non-responders in the present study. Table S12. Predictive performance of 10-analyte signature, non-invasive variables and standard measurements. Table S13. Differential analytes between T2D and no T2D women in the IBF group.
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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 | 0.002 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.745 | 0.067 |
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".