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Record W3084362742 · doi:10.6084/m9.figshare.12318608

A comparison of mobility assistive devices for elderly and patients with lower limb injury: Narrative Review

2020· article· en· W3084362742 on OpenAlexaff
Paroma Arefin, Md Shehan Habib, Aishawarya Arefin, Saidul Arefin

Bibliographic record

VenueOpen MIND · 2020
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsHappinessContext (archaeology)Independence (probability theory)Assistive technologyLimited mobilityAssistive devicePhysical medicine and rehabilitationIndependent livingInternet privacyPsychologyComputer scienceMedicineGerontologyHuman–computer interactionSocial psychology

Abstract

fetched live from OpenAlex

Older adults, people with chronic conditions, the disabled, and people with mobility impairments suffer from movement difficulties and have the risk of falling. Mobility aids alleviate the impact of mobility limitations or improve independence and reduce the burden of care and help people with mobility problems to move around enjoy greater freedom and independence. These assistive devices are essential to facilitate more movement independence and reduce pain and boost self-confidence and happiness in the regular life of the elderly. In this review paper, we have addressed the context and need for mobility aids. The main objective of the research was to explore different options of mobility assistive devices and compare their advantages and drawbacks. The study found standard cane as the most popular assistive device of all due to its user convenience. This study can help to find out the limitations of different mobility aids and help to improve features to increase user convenience.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.050
GPT teacher head0.387
Teacher spread0.338 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2020
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicStroke Rehabilitation and RecoveryFrench-language works237,207