Muscle pain induced by hypertonic saline in the knee extensors decreases single-limb isometric time to task failure
Bibliographic record
Abstract
PURPOSE: Increased nociceptive activity and the experience of exercise-induced pain (EIP) may contribute to fatigue during endurance exercise. To investigate this, a pain model that produces pain similar to EIP and decouples its relationship to exercise intensity is required. This study (1) compared the quality of pain caused by a hypertonic saline injection into the vastus lateralis in resting and exercise conditions, and (2) investigated whether this pain contributes to changes in time to task failure. METHODS: On separate days, 18 participants completed a time to task failure at 20% maximal voluntary torque (MVT), a resting hypertonic saline intramuscular injection, and in a further three visits a time to task failure at 10% MVT following injection of isotonic saline, hypertonic saline or a control (no injection). RESULTS: In a subset of eligible participants (n = 12), the hypertonic saline combined with 10% MVT produced a qualitative experience of pain (assessed by the McGill Pain Questionnaire) that felt similar to EIP. 10% MVT with hypertonic saline significantly elevated pain intensity in the first 20% of the time to task failure and caused a significantly (P < 0.05) shorter time to task failure (448 ± 240 s) compared with the isotonic saline (605 ± 285 s) and control (514 ± 197 s) conditions. CONCLUSION: These findings demonstrate that hypertonic saline increases the intensity of pain during exercise, which results in a faster occurrence of exercise-induced fatigue. These results provide important evidence supporting pain as a limiting factor in endurance performance.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".