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Acute effects of sustained isometric knee extension on cerebral and muscle oxygenation responses

2009· article· en· W1998932452 on OpenAlexaff
Marta Pereira, Paulo Sérgio Chagas Gomes, Yagesh Bhambhani

Bibliographic record

VenueClinical Physiology and Functional Imaging · 2009
Typearticle
Languageen
FieldMedicine
TopicCardiovascular and exercise physiology
Canadian institutionsUniversity of Alberta
FundersFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro
KeywordsIsometric exerciseMedicineCardiologyOxygenationAnaerobic exerciseInternal medicineVastus lateralis muscleContraction (grammar)AnesthesiaPhysical therapySkeletal muscle

Abstract

fetched live from OpenAlex

Cerebral contra-lateral frontal lobe and the dominant vastus lateralis muscle oxygenation (Cox, Mox) and blood volume (Cbv, Mbv) were recorded simultaneously using near infrared spectroscopy (NIRS) in 12 healthy volunteers (37.4 +/- 9.9 years; 72.3 +/- 16.1 kg; 171.0 +/- 9.6 cm) during 2 min resting baseline, an isometric knee extension with the 1 RM load sustained to the point of fatigue, and 3 min recovery. The mean exercise duration was 19.1 +/- 2.6 s. During the contraction, Cox and Cbv increased systematically with no sign of levelling off until the point of fatigue. In contrast, Mox and Mbv declined continuously until the termination of exercise. Qualitative analysis of these NIRS profiles suggested that maximal isometric performance under normoxic conditions was most likely not limited by central neuronal activation, but rather, was due to factors within the exercising muscle. It is likely that depletion of intramuscular stores of high energy phosphates and oxymyoglobin, as well as the accumulation of metabolites from anaerobic pathways, were implicated in fatigue during this sustained high intensity isometric contraction.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.018
GPT teacher head0.313
Teacher spread0.295 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2009
Admission routes1
Has abstractyes

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