A randomized pilot study of dietary treatments for polycystic ovary syndrome in adolescents
Bibliographic record
Abstract
BACKGROUND: Evidence is lacking to recommend one diet over another when treating polycystic ovary syndrome (PCOS). OBJECTIVES: To obtain preliminary data, comparing the impact of a low-glycaemic load (LGL) vs. low-fat (LF) diet on biochemical hyperandrogenism in overweight and obese adolescents with PCOS. To ascertain feasibility of recruiting study participants, in partnership with an adolescent clinic, and implementing dietary interventions. METHODS: Randomized controlled trial of 19 overweight and obese adolescents with PCOS and not using hormonal contraceptives (HCs). Interventions comprised nutrition education, dietary counselling and cooking workshops to foster adherence to a LGL (45% carbohydrate, 35% fat, 20% protein) or LF (55% carbohydrate, 25% fat, 20% protein) diet over 6 months. Serum bioavailable testosterone was the primary outcome. RESULTS: Sixteen (LGL, n = 7; LF, n = 9) participants completed the study. Body fat percentage decreased (P < 0.05) in response to the interventions, with no difference between the LGL and LF groups (-1.2% vs. -2.2%; P = 0.16). Bioavailable testosterone did not change for either group (-0.4 vs. -1.8 ng dL(-1) ; P = 0.35). Regarding feasibility, recruiting adolescents posed a challenge, and use of HCs was a main reason for ineligibility. Participants attended 5.9 of 6 in-person visits and 2.6 of 3 cooking workshops, completed 4.9 of 6 telephone counselling calls, and reported high satisfaction with the diets and cooking workshops (≥8 on a 10-cm scale). CONCLUSIONS: Dietary interventions were beneficial for weight control but did not attenuate biochemical hyperandrogenism. Innovative strategies are needed to recruit adolescents for studies aimed at assessing independent effects of diet on features of PCOS.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".