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Record W2773780060 · doi:10.3390/healthcare5040092

The Impact of Hemodialysis on Spatio-Temporal Characteristics of Gait and Role of Exercise: A Systematic Review

2017· review· en· W2773780060 on OpenAlexaff
Anuradha Sawant, Tom J. Overend

Bibliographic record

VenueHealthcare · 2017
Typereview
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsWestern UniversityLondon Health Sciences Centre
Fundersnot available
KeywordsGaitConfidence intervalPhysical medicine and rehabilitationMedicineHemodialysisPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

Background: People with end-stage kidney disease (ESKD) on hemodialysis (HD) commonly have functional impairments. The purpose of this systematic review was to evaluate the effect of HD on spatio-temporal characteristics of gait, and effect of exercise on these parameters. Methods: Electronic databases were searched to identify relevant citations. Extracted data was computed using a random effects model for means (Hedges’ and 95% confidence interval (CI). Results: 27 studies met inclusion criteria. Mean values: gait speed (GS)—1.0 m/s (CI: 0.9–1.1 m/s; 16 studies), fast walking speed (FWS)—1.5 m/s (CI: 1.3–1.6 m/s; 7 studies), timed get-up & go test (TUG) —6.8 s (CI: 6.1–7.5 s; 2 studies), walk tests (WT) 193.0 s (CI: 116.0–270.0; 5 studies), 6 min-walk-test (6MWT)—386.6 m (CI: 243.2–530.0 m; 11 studies). 4 studies compared participants on HD with normal controls and 10 studies evaluated the effect of nutrition/exercise. Conclusions: Compared to age-matched populations, people with ESKD/HD had significantly slower GS and reduced walk distances; with intervention, the change in the distance walked was significant. Further research is required to evaluate the effect of HD on gait parameters, and the type of exercise/nutrition that will lead to meaningful changes.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.118
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0000.000
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 head0.082
GPT teacher head0.453
Teacher spread0.372 · 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 designSystematic review
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

Citations7
Published2017
Admission routes1
Has abstractyes

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