Bibliographic record
Abstract
OBJECTIVE: To examine acupuncture's effect on cycling performance. DESIGN: This was a prospective, single-blind, patient as own control (repeated measures), crossover design. Subjects underwent 3 tests a week, riding a stationary bike for 20-km as fast as able. Before each test, they received acupuncture (test A), "sham" acupuncture (test B), and no intervention (control, test C) once each in a random order. SETTING: University of Alberta, Faculty of Rehabilitation Medicine. PARTICIPANTS: 20 male cyclists (age, 18 to 30 years) were recruited via convenience sampling of students and general public. Athletic ability was assessed through a questionnaire and modified Par-Q. INTERVENTIONS: Acupuncture, sham acupuncture, and no intervention in random order with each subject before each test. Acupuncture points were chosen on the basis of Traditional Chinese Medicine and administered immediately before cycling. Sham was shallow needling of known acupoints. MAIN OUTCOME MEASUREMENTS: The outcome measures of each of the tests were time to completion, VAS for lower extremity/exercise-induced pain, Borg rating of perceived exertion (RPE), and blood lactate concentrations, recorded immediately following each test. RESULTS: Mean times to Test A, B, and C completion were 36.19 +/- 5.23, 37.03 +/- 5.66, and 37.48 +/- 6.00 minutes, respectively, P = 0.76. Mean RPE scores after tests A, B, and C were 17.65 +/- 0.67, 16.95 +/- 0.99, and 16.85 +/- 0.88, respectively, P = 0.0088. Mean VAS scores after tests A, B, and C were 7.72 +/- 0.86, 7.94 +/- 0.78, and 8.08 +/- 0.69, respectively, P = 0.76. CONCLUSIONS: The only statistically significant finding was that acupuncture gave higher RPE scores compared to the other tests. The clinical significance was that the higher RPE scores gave lower time and VAS scores.
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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".