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Record W2810588104 · doi:10.1139/apnm-2018-0202

Physiological responses to partial-body cryotherapy performed during a concurrent strength and endurance session

2018· article· en· W2810588104 on OpenAlexvenueno aff
Alessandro Piras, Francesco Campa, Stefania Toselli, Rocco Di Michele, Milena Raffi

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2018
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsnot available
Fundersnot available
KeywordsCardiorespiratory fitnessCryotherapyMedicineHeart rateVentilation (architecture)VO2 maxEndurance trainingCardiologyAnesthesiaInternal medicineSurgeryBlood pressure

Abstract

fetched live from OpenAlex

) 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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.783
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.315
Teacher spread0.290 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations19
Published2018
Admission routes1
Has abstractyes

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