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Skeletal Muscle Strength and Endurance in Recipients of Lung Transplants

2008· article· en· W142949438 on OpenAlexaff
Sunita Mathur, Robert D. Levy, Darlene Reid

Bibliographic record

VenueCardiopulmonary Physical Therapy Journal · 2008
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsVancouver Native Health SocietyUniversity of British Columbia
Fundersnot available
KeywordsMedicineSkeletal muscleLungAnatomyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Exercise limitation in recipients of lung transplant may be a result of abnormalities in the skeletal muscle. However, it is not clear whether these abnormalities are merely a reflection of the changes observed in the pretransplant condition. The purpose of this paper was to compare thigh muscle volume and composition, strength, and endurance in lung transplant recipients to people with chronic obstructive pulmonary disease (COPD). METHODS: Single lung transplant recipients (n=6) and people with COPD (n=6), matched for age, sex, and BMI participated in the study. Subjects underwent MRI to determine muscle size and composition, lower extremity strength testing and an isometric endurance test of the quadriceps. RESULTS: Lung transplant recipients had similar muscle volumes and intramuscular fat infiltration of their thigh muscles and similar strength of the quadriceps and hamstrings to people with COPD who had not undergone transplant. However, quadriceps endurance tended to be lower in transplant recipients compared to people with COPD (15 +/- 7 seconds in transplant versus 31 +/- 12 seconds in COPD, p = 0.08). CONCLUSIONS: Recipients of lung transplant showed similar changes in muscle size and strength as people with COPD, however muscle endurance tended to be lower in people with lung transplants. Impairments in muscle endurance may reflect the effects of immunosuppressant medications on skeletal muscle in people with lung transplant.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.245
Threshold uncertainty score0.475

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 head0.029
GPT teacher head0.305
Teacher spread0.276 · 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

Citations35
Published2008
Admission routes1
Has abstractyes

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