Effects of Pseudoephedrine on Maximal Cycling Power and Submaximal Cycling Efficiency
Bibliographic record
Abstract
PURPOSE: To study the effects of a therapeutic dose of pseudoephedrine on anaerobic cycling power and aerobic cycling efficiency. METHODS: Eleven healthy moderately trained males (VO (2peak) 4.4 +/- 0.8 L x min(-1) participated in a double-blinded crossover design. Subjects underwent baseline (B) tests for anaerobic (Wingate test) and aerobic (VO (2peak) test) cycling power. Subjects ingested either 60 mg of pseudoephedrine hydrochloride (D) or a placebo (P) and, after 90 min of rest, a Wingate and a cycling efficiency test were performed. During the cycling efficiency test, heart rate (HR) and VO(2) were averaged for the last 5 min of a 10-min cycle at 40% and 60% of the peak power achieved during the VO (2peak) test. RESULTS: There were no significant differences in peak power (B = 860 +/- 154, D = 926 +/- 124, P = 908 +/- 118 W), total work (B = 20 +/- 3, D = 21 +/- 3, P = 21 +/- 3 kJ), or fatigue index (B = 39 +/- 8, D = 45 +/- 5, P = 43 +/- 5%). There were no significant differences in HR at 40% power (D = 138 +/- 10, P = 137 +/- 10 beats.min-1) or 60% power (D = 161 +/- 11, P = 160 +/- 11 beats x min(-1). There were no significant differences in cycling efficiency at 40% power (D = 18.8 +/- 1.8, P = 18.5 +/- 1.8%) or 60% power (D = 20.3 +/- 2.0, P = 20.1 +/- 2.1%). CONCLUSION: A therapeutic dose of pseudoephedrine hydrochloride does not affect anaerobic cycling performance or aerobic cycling efficiency.
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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) | 0.001 | 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".