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Record W4399547705 · doi:10.1183/20734735.0347-2023

Increasing exercise capacity and physical activity in the COPD patient

2024· article· en· W4399547705 on OpenAlexaff
Antarpreet Kaur, Jean Bourbeau, Lisa Jane Brighton, Bartolomé R. Celli, R. H. Crouch, Heleen Demeyer, Daniel A. Gerardi, Hideki Katsura, Paula Meek, Mike Morgan, Mara Paneroni, Sally Singh, Michael K. Stickland

Bibliographic record

VenueBreathe · 2024
Typearticle
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsCovenant HealthUniversity of AlbertaMcGill University Health Centre
FundersEconomic and Social Research Council
KeywordsPsychological interventionPulmonary rehabilitationCOPDPhysical therapyIntervention (counseling)MedicinePhysical activityRehabilitationPhysical medicine and rehabilitationIntensive care medicineNursingPsychiatry

Abstract

fetched live from OpenAlex

Higher levels of exercise capacity and physical activity are desired outcomes in the comprehensive management of the COPD patient. In addition, improvements in exercise capacity and physical activity are instrumental to optimising other important therapeutic goals, such as improved health status, reduced healthcare utilisation and increased survival. Four general approaches towards increasing exercise capacity and physical activity in individuals with COPD will be discussed in this review: 1) pharmacological intervention, especially the administration of long-acting bronchodilators; 2) pulmonary rehabilitation, including exercise training and collaborative self-management; 3) behavioural interventions; and 4) web-based interventions. These are by no means the only approaches, nor are they mutually exclusive: indeed, combining them, as necessary, to meet the needs of the individual respiratory patient may promote optimal outcomes, although further research is necessary in this area.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.944
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.023
GPT teacher head0.297
Teacher spread0.274 · 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

Citations18
Published2024
Admission routes1
Has abstractyes

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