Trajectory and determinants of change in lean soft tissue over the postpartum period
Bibliographic record
Abstract
The aim of this study was to characterise changes in lean soft tissue (LST) and examine the contributions of energy intake, physical activity and breast-feeding practices to LST changes at 3 and 9 months postpartum. We examined current weight, LST (via dual-energy X-ray absorptiometry), dietary intake (3-d food diary), physical activity (Baecke questionnaire) and breast-feeding practices (3-d breast-feeding diary) in forty-nine women aged 32·9 (sd 3·8) years. Changes in LST varied from -2·51 to +2·50 kg with twenty-nine women gaining LST (1·1 (sd 0·7) kg, P<0·001) and twenty women losing LST (-0·9 (sd 0·8) kg, P<0·001). Energy intake (133 (SD 42) v. 109 (SD 33) kJ/kg, P=0·019) and % kJ from fat at 3 months postpartum was higher in women who gained LST at 9 months postpartum (gained LST=34 (sd 5) % kJ; lost LST=29 (sd 4) % kJ, P=0·002). Women who gained LST reported breast-feeding their infants more frequently (gained LST=8 (sd 3) feeds/d; lost LST=5 (sd 1) feeds/d, P=0·014) and for more time per d (gained LST=115 (sd 78) min/d; lost LST=59 (sd 34) min/d, P=0·016) at 9 months postpartum. Energy intake and % kJ from fat at 3 months were significant predictors of LST gain (β=0·08 (se 0·04) and 0·24 (se 0·09), respectively). This suggests that gain in LST may be associated with more frequent and longer episodes of breast-feeding at 9 months postpartum as well as dietary intake early in the postpartum period.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".