A pulse‐based diet and exercise training in women with polycystic ovarian syndrome: effects on body composition, blood lipids and reproductive measures (117.5)
Bibliographic record
Abstract
Polycystic ovarian syndrome (PCOS) is an endocrine disorder that predisposes women to an increased risk of heart disease, diabetes, infertility and endometrial cancer with an annual cost of $4 billion in the United States. We hypothesized that a pulse‐based diet (e.g. beans, lentils) would have a positive effect on body composition as analyzed by dual energy X‐ray absorptiometry, reproductive measures and serum lipid profiles. Twenty‐five women with PCOS aged 18‐35y with a mean BMI of 31 were randomly assigned to groups receiving a pulse‐based diet (n=14) or the National Cholesterol Education Program (NCEP) therapeutic lifestyle changes (TLC) diet (n=11) for 16 wks while participating in an exercise program. Following the intervention, both groups lost body mass (p<0.05; Pulse ‐2.4 vs TLC ‐3.0 kg), percent fat mass (Pulse ‐1.0 vs TLC ‐1.6 %) and trunk fat mass (Pulse ‐1.0 vs TLC ‐1.7 kg). No changes were observed in lean body mass between groups. Both dietary interventions also resulted in more women exhibiting regular menstrual patterns (p<0.001) and a tendency towards a decreased antral follicle count in the right ovary (p=0.06); however, only the pulse diet reduced total cholesterol to HDL ratio (4.2 to 3.8 p<0.005). As hypothesized, a pulse‐based diet reduced body fat, and improved reproductive measures and serum lipid profiles. Thus, early diagnosis and dietary/exercise interventions are important in alleviating both the personal health and economic costs associated with PCOS. Grant Funding Source : Supported by the Saskatchewan Pulse Growers and Agriculture Agri‐Food Canada (Cluster Program)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".