Effects of cold water immersion on circulating inflammatory markers at the Kona Ironman World Championship
Bibliographic record
Abstract
Cold water immersion (CWI) purportedly reduces inflammation and improves muscle recovery after exercise, yet its effectiveness in specific contexts (ultraendurance) remains unclear. Thus, our aim was to study hematological profiles, systemic inflammation, and muscle damage responses to a specific post-race CWI (vs. control) during recovery after the Ironman World Championship, a culmination of ∼100 000 athletes competing in global qualifying Ironman events each year. Twenty-nine competitors were randomized into either a CWI or control (CON) group. Physiological parameters and blood samples were taken at pre-race, after intervention (POST), and 24 (+1DAY) and 48 hours (+2DAY) following the race. Muscle damage markers (plasma myoglobin, serum creatine kinase) were elevated at POST, +1DAY, and +2DAY, while inflammatory cytokines interleukin (IL)-6, IL-8, and IL-10 and total leukocyte counts were increased only at POST. CWI had no effect on these markers. Numbers of the most abundant circulating cell type, neutrophils, were elevated at POST more so in CWI (p < 0.05, vs. CON). Despite that neutrophil counts may be a sensitive marker to detect subtle effects, CWI does not affect recovery markers 24- and 48-hours post-race (vs. CON). Overall, we determined that our short CWI protocol was not sufficient to improve recovery. Novelty: Ironman World Championship event increased circulating muscle damage markers, inflammatory markers, and hematological parameters, including circulating immune cell sub-populations that recover 24–48 hours after the race. 12-min CWI post-ultraendurance event affects the absolute numbers of neutrophils acutely, post-race (vs. CON), but does not impact recovery 24- and 48-hours post-race.
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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) | 0.001 | 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 | 0.001 |
| 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".