Physiological responses to partial-body cryotherapy performed during a concurrent strength and endurance session
Bibliographic record
Abstract
) were randomly exposed to 2 different conditions: (i) PBC: 3 min at -160 °C, and (ii) passive recovery at 21 °C. We performed the bioelectrical impedance analysis (BIA) and recorded temperature and cardiac autonomic variables at 3 time points: at baseline, after strength training, and after 90 min of recovery. In addition, blood lactate concentration was measured 1 min before and 2.5 min after the IR. Heart rate (HR), energy cost, minute ventilation, oxygen uptake, and metabolic power were assessed during the IR. Homeostatic hydration status was affected by the execution of an intense strength training subsession. Then, after PBC, the BIA vector was restored close to normohydration status. Autonomic variables changed over time in both conditions, although the mean differences and effect sizes were greater in the PBC condition. During IR, HR was 3.5% lower after PBC, and the same result was observed for oxygen uptake (∼4.9% lower) and ventilation (∼6.5% lower). The energy cost measured after cryotherapy was ∼9.0% lower than after passive recovery. Cryotherapy enhances recovery after a single strength training session, and during subsequent IR, it shows a reduction in cardiorespiratory and metabolic parameters. PBC may be useful for those athletes who compete or train more than once in the same day, to improve recovery between successive training sessions or competitions.
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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) | 0.000 | 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) | 0.005 | 0.001 |
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".