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Record W2529018438 · doi:10.1183/13993003.00347-2016

Exercise, ageing and the lung

2016· review· en· W2529018438 on OpenAlexaff
Michael A. Roman, Harry B. Rossiter, Richard Casaburi

Bibliographic record

VenueEuropean Respiratory Journal · 2016
Typereview
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsRockyview General HospitalUniversity of Calgary
Fundersnot available
KeywordsMedicineAgeingPulmonary function testingLimitingCardiologyVentilation (architecture)LungInternal medicinePhysical therapyPhysical medicine and rehabilitationIntensive care medicine

Abstract

fetched live from OpenAlex

This review provides a pulmonary-focused description of the age-associated changes in the integrative physiology of exercise, including how declining lung function plays a role in promoting multimorbidity in the elderly through limitation of physical function. We outline the ageing of physiological systems supporting endurance activity: 1) coupling of muscle metabolism to mechanical power output; 2) gas transport between muscle capillary and mitochondria; 3) matching of muscle blood flow to its requirement; 4) oxygen and carbon dioxide carrying capacity of the blood; 5) cardiac output; 6) pulmonary vascular function; 7) pulmonary oxygen transport; 8) control of ventilation; and 9) pulmonary mechanics and respiratory muscle function. Deterioration in function occurs in many of these systems in healthy ageing. Between the ages of 25 and 80 years pulmonary function and aerobic capacity each decline by ∼40%. While the predominant factor limiting exercise in the elderly likely resides within the function of the muscles of ambulation, muscle function is (at least partially) rescued by exercise training. The age-associated decline in pulmonary function, however, is not recovered by training. Thus, loss in pulmonary function may lead to ventilatory limitation in exercise in the active elderly, limiting the ability to accrue the health benefits of physical activity into senescence.

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)0.003
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.044
GPT teacher head0.342
Teacher spread0.298 · 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 designOther design
Domainnot available
GenreReview

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

Citations199
Published2016
Admission routes1
Has abstractyes

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