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Record W2204324825 · doi:10.1093/ptj/81.11.1790

Effect of 15% Body Weight Support on Exercise Capacity of Adults Without Impairments

2001· article· en· W2204324825 on OpenAlexaff
Marilyn MacKay-Lyons, Lydia Makrides, Stephanie Speth

Bibliographic record

VenuePhysical Therapy · 2001
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPhysical medicine and rehabilitationBody weightPhysical therapyPsychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND AND PURPOSE: External support of body weight, a technique used for the gait training of patients with neurologic conditions, may also be beneficial for tests of exercise capacity in people whose impairments in motor function and balance have traditionally precluded such testing. The purpose of this study was to investigate the effect of using external support of 15% of body mass during treadmill exercise testing of adults without impairments. SUBJECTS AND METHODS: Seven men and 8 women (mean age=55.2 years, SD=11.3, range=43-82) performed 3 treadmill tests with random assignment of testing condition: (1) no body weight support (BWS)-standard test, (2) 0% BWS-harness in place but no use of external support, and (3) 15% BWS-use of external support for 15% of body mass. Expired gas was analyzed to determine oxygen uptake, carbon dioxide production, minute ventilation, tidal volume, heart rate, and respiratory exchange ratio. RESULTS: Use of external support for 15% of body mass did not affect the end-expiratory gas exchange variables, although the time to achieve peak values was lengthened. Maximal tidal volume was lower in the 15% BWS test, but maximal minute ventilation was not different. DISCUSSION AND CONCLUSION: Because 15% BWS did not affect the exercise capacity of adults without known impairments, future study of its application to testing of patients with neurologic injuries is warranted.

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 score0.443
Threshold uncertainty score0.518

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 head0.019
GPT teacher head0.364
Teacher spread0.345 · 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

Citations34
Published2001
Admission routes1
Has abstractyes

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