Creatine co‐ingestion with carbohydrate or cinnamon extract provides no added benefit to anaerobic performance
Bibliographic record
Abstract
The insulin response following carbohydrate ingestion enhances creatine transport into muscle. Cinnamon extract is promoted to have insulin-like effects, therefore this study examined if creatine co-ingestion with carbohydrates or cinnamon extract improved anaerobic capacity, muscular strength, and muscular endurance. Active young males (n = 25; 23.7 ± 2.5 y) were stratified into 3 groups: (1) creatine only (CRE); (2) creatine+ 70 g carbohydrate (CHO); or (3) creatine+ 500 mg cinnamon extract (CIN), based on anaerobic capacity (peak power·kg(-1)) and muscular strength at baseline. Three weeks of supplementation consisted of a 5 d loading phase (20 g/d) and a 16 d maintenance phase (5 g/d). Pre- and post-supplementation measures included a 30-s Wingate and a 30-s maximal running test (on a self-propelled treadmill) for anaerobic capacity. Muscular strength was measured as the one-repetition maximum 1-RM for chest, back, quadriceps, hamstrings, and leg press. Additional sets of the number of repetitions performed at 60% 1-RM until fatigue measured muscular endurance. All three groups significantly improved Wingate relative peak power (CRE: 15.4% P = .004; CHO: 14.6% P = .004; CIN: 15.7%, P = .003), and muscular strength for chest (CRE: 6.6% P < .001; CHO: 6.7% P < .001; CIN: 6.4% P < .001), back (CRE: 5.8% P < .001; CHO: 6.4% P < .001; CIN: 8.1% P < .001), and leg press (CRE: 11.7% P = .013; CHO: 10.0% P = .007; CIN: 17.3% P < .001). Only the CRE (10.4%, P = .021) and CIN (15.5%, P < .001) group improved total muscular endurance. No differences existed between groups post-supplementation. These findings demonstrate that three different methods of creatine ingestion lead to similar changes in anaerobic power, strength, and endurance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".