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Record W2162548247 · doi:10.1002/ppul.20899

Non‐invasive assessment of exercise performance in children with cystic fibrosis (CF) and non‐cystic fibrosis bronchiectasis: Is there a CF specific muscle defect?

2009· article· en· W2162548247 on OpenAlexaff
Mark Rosenthal, Indra Narang, Liz Edwards, Andrew Bush

Bibliographic record

VenuePediatric Pulmonology · 2009
Typearticle
Languageen
FieldMedicine
TopicCystic Fibrosis Research Advances
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsCystic fibrosisMedicineBronchiectasisFibrosisIntensive care medicineInternal medicinePediatricsLung

Abstract

fetched live from OpenAlex

INTRODUCTION: Peripheral muscle dysfunction is increasingly recognized as complicating respiratory disease, but this is difficult to measure non-invasively. RESEARCH QUESTION: Can skeletal muscle function and efficiency be measured during exercise non-invasively using respiratory mass spectrometry (RMS); and is the known exercise dysfunction in cystic fibrosis (CF) children related in part to a disease specific defect of skeletal muscle, or a non-specific manifestation of chronic airway infection and inflammation. METHODS: Calculations of effective pulmonary blood flow and stroke volume, blood oxygen content and oxygen dispatch from the lungs, skeletal muscle oxygen extraction and consumption, anerobic threshold and capacity, and gross, net and work efficiency in 106 controls and 36 children (18 CF) with bronchiectasis, all aged from 8 to 17 years. RESULTS: Normal values for control subjects are tabulated. CF and non-CF bronchiectatic subjects had similar physiology, and skeletal muscle abnormalities could not be detected. Reduced oxygen dispatch from the lungs, due to an inability to raise stroke volume, without an increase in functional residual capacity was the major factor in reduced exercise ability. CONCLUSIONS: Non-invasive RMS can be used to determine skeletal muscle function in children. The changes observed in CF subjects were very similar to non-CF bronchiectatic subjects and thus a CF specific defect was not demonstrated.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.258
Teacher spread0.250 · 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.

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

Citations31
Published2009
Admission routes1
Has abstractyes

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