Gender differences in carbohydrate loading are related to energy intake
Bibliographic record
Abstract
We demonstrated that female endurance athletes did not increase their muscle glycogen concentration after an increase in the dietary carbohydrate intake (58 --> 74%), whereas men did (Tarnopolsky MA, SA Atkinson, SM Phillips, and JD McDougall, J Appl Physiol 78: 1360-1368, 1995). This may have been related to a lower energy or carbohydrate intake by the women or due to an inherent gender difference in glycogen storage capacity. We examined whether well-trained men (n = 6) and women (n = 6) increased muscle glycogen concentration after an increase in both the relative (58 --> 75%) and absolute energy and carbohydrate intake and whether potential gender differences were related to muscle hexokinase enzyme activity. Subjects were randomly allocated to three diets [Hab, habitual; CHO, high carbohydrate (75%); and CHO + E, extra energy + CHO ( upward arrow~34%)] for a 4-day period before a muscle biopsy for analysis of total and pro- and macroglycogen and hexokinase activity. Total glycogen concentration was higher for the men on the CHO and CHO + E trials compared with Hab (P < 0.05), whereas women increased only on the CHO + E trial compared with Hab (P < 0.05). There were no gender differences in the proportion of pro- and macroglycogen or hexokinase activity. A low energy intake may explain the previously reported lower capacity for women to glycogen load compared with men.
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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) | 0.004 | 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".