Acetaminophen ingestion improves muscle activation and performance during a 3-min all-out cycling test
Bibliographic record
Abstract
Acute acetaminophen (ACT) ingestion has been shown to enhance cycling time-trial performance. The purpose of this study was to assess whether ACT ingestion enhances muscle activation and critical power (CP) during maximal cycling exercise. Sixteen active male participants completed two 3-min all-out tests against a fixed resistance on an electronically braked cycle ergometer 60 min after ingestion of 1 g of ACT or placebo (maltodextrin, PL). CP was estimated as the mean power output over the final 30 s of the test and W′ (the curvature constant of the power–duration relationship) was estimated as the work done above CP. The femoral nerve was stimulated every 30 s to measure membrane excitability (M-wave) and surface electromyography (EMGRMS) was recorded continuously to infer muscle activation. Compared with PL, ACT ingestion increased CP (ACT: 297 ± 32 W vs. PL: 288 ± 31 W, P < 0.001) and total work done (ACT: 66.4 ± 6.5 kJ vs. PL: 65.4 ± 6.4 kJ, P = 0.03) without impacting W′ (ACT: 13.1 ± 2.9 kJ vs. PL: 13.6 ± 2.4 kJ, P = 0.19) or the M-wave amplitude (P = 0.66) during the 3-min all-out cycling test. Normalised EMGRMS amplitude declined throughout the 3-min protocol in both PL and ACT conditions; however, the decline in EMGRMS amplitude was attenuated in the ACT condition, such that the EMGRMS amplitude was greater in ACT compared with PL over the last 60 s of the test (P = 0.04). These findings indicate that acute ACT ingestion might increase performance and CP during maximal cycling exercise by enhancing muscle activation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".