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Record W2906046546 · doi:10.1177/1479973118820311

Balance assessment in people with COPD: An evidence-based guide

2018· review· en· W2906046546 on OpenAlexafffund
Marla Beauchamp

Bibliographic record

VenueChronic Respiratory Disease · 2018
Typereview
Languageen
FieldMedicine
TopicChronic Obstructive Pulmonary Disease (COPD) Research
Canadian institutionsMcMaster UniversityWest Park Healthcare Centre
FundersCenters for Disease Control and PreventionReseau canadien de recherche respiratoire
KeywordsMedicineBalance (ability)COPDPulmonary rehabilitationDynamic balancePhysical therapyPulmonary diseaseRehabilitationIntensive care medicineClinical PracticeTest (biology)Physical medicine and rehabilitationPsychiatry

Abstract

fetched live from OpenAlex

Balance problems and an increased rate and risk of falls are common in people with chronic obstructive pulmonary disease (COPD). Although a balance assessment is now recommended by clinical practice guidelines for pulmonary rehabilitation, specific tests have yet to be suggested. The purpose of this review is to provide an evidence-based synthesis of balance measurement in older adults and in people with COPD, to guide practice in this area. An overview of best practices for assessing balance and fall risk in older adults is provided along with a practical synthesis of evidence to date on common balance measures used in people with COPD such as the Timed Up and Go, Single Leg Stance, Berg Balance, and Mini and Full Balance Evaluation Systems Tests. Finally, two clinical scenarios are described to illustrate the process of evidence-based clinical decision-making with respect to balance assessment in people with COPD. Ultimately, the selection of balance test and its interpretation will depend on the purpose of the assessment, available data on psychometric properties, the patient's individual characteristics, and the resources available to the clinician.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.876
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.074
GPT teacher head0.407
Teacher spread0.333 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations41
Published2018
Admission routes2
Has abstractyes

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