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Effects of low load/high-repetition resistance training in patients with COPD

2017· article· en· W2780439491 on OpenAlexaff
Didier Saey, Mickaël Martin, François Maltais

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsMedicineIsometric exerciseCOPDPhysical therapyResistance trainingPhysical medicine and rehabilitationLeg pressInternal medicine

Abstract

fetched live from OpenAlex

We investigated the effects of 8 weeks of low load/high-repetitive resistance training (LLHR) of 6 upper and lower limbs exercises in 21 patients with COPD (FEV1 38% predicted). Outcome measurements were shoulder flexion (SF) and knee extension (KE) strength and endurance, distance walked on the 6-minute walk test (6MWD), arm function measured with the unsupported upper extremity exercise test (UULEX) and impact of COPD on daily life using the COPD assessment test (CAT). In addition, dyspnea and fatigue ratings from the 6 LLHR exercises were collected during two identical exercise sessions with matched workloads that were performed before and after the intervention period. Quadriceps muscle fatigue was also assessed by measuring the decline of both Isometric KE maximal voluntary contraction (ΔMVC) and potentiated twitch force (ΔTW) measured before and after these two sessions. Table. Effects of 8 weeks of LLHR on study outcomes Eight weeks of LLHR improved limb muscle function, functional performance and CAT score. In addition, exertional symptoms and amount of quadriceps contractile fatigue were reduced when comparing to an identical exercise session with matched workloads performed before and after the intervention period

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: Non-randomized trial · 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)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.009
GPT teacher head0.260
Teacher spread0.251 · 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 designNon-randomized trial
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

Citations0
Published2017
Admission routes1
Has abstractyes

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