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Record W3017778588 · doi:10.17338/trainology.9.1_33

The impact of a single bout of intermittent pneumatic compression on performance, inflammatory markers, and myoglobin in football athletes

2020· article· en· W3017778588 on OpenAlexafffund
Jeremie E. Chase, Jason Peeler, Matthew J. Barr, Phillip F. Gardiner, Stephen M. Cornish

Bibliographic record

VenueJournal of Trainology · 2020
Typearticle
Languageen
FieldMedicine
TopicExercise and Physiological Responses
Canadian institutionsUniversity of Manitoba
FundersUniversity of Manitoba
KeywordsMedicineAthletesDelayed onset muscle sorenessMyoglobinMuscle damagePhysical therapyInflammationPhysical medicine and rehabilitationPopulationInternal medicineBiology

Abstract

fetched live from OpenAlex

Objective: Intermittent Pneumatic Compression (IPC) use as a tool for recovery after exercise has recently become widespread among athletes. While there is anecdotal support for IPC, little research has been done to show its effectiveness in recovery. This study examined the impact of IPC use for recovery on performance, markers of inflammation, and a marker of muscle damage.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.921
Threshold uncertainty score0.158

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.034
GPT teacher head0.294
Teacher spread0.260 · 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 designObservational
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

Citations3
Published2020
Admission routes2
Has abstractyes

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