MétaCan
Menu
Back to cohort
Record W2298432641 · doi:10.1177/2055668316636316

Functional level assessment of individuals with transtibial limb loss: Evaluation in the clinical setting versus objective community ambulatory activity

2016· article· en· W2298432641 on OpenAlexaff
Michael S. Orendurff, Silvia Raschke, Lorne Winder, David Moe, David Boone, Toshiki Kobayashi

Bibliographic record

VenueJournal of Rehabilitation and Assistive Technologies Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsBritish Columbia Institute of Technology
Fundersnot available
KeywordsAmbulatoryMedicinePhysical therapyGaitPhysical medicine and rehabilitationCohortSurgeryInternal medicine

Abstract

fetched live from OpenAlex

The functional level (K level) of prosthetic users is used to choose appropriate prosthetic components, but ratings may highly subjective. A more objective and robust method to determine K level may be appealing. The aim of this study was to determine the relationship between K level determined in the clinic to K level based on real world ambulatory activity data collected by StepWatch. Twelve individuals with transtibial limb loss gave informed consent to participate. K level assessments performed in the clinic by a single treating prosthetist were compared with a calculated estimate based on seven days of real world ambulatory activity patterns using linear regression. There was good agreement between the two methods of determining K level with R 2 = 0.775 ( p < 0.001). The calculated estimate of K level based on actual ambulatory activity in real world settings appears to be similar to the treating prosthetist’s assessment of K level based on gait observation and patient responses in the clinic. Clinic-based ambulatory capacity in transtibial prosthetic users appears to correlate with real world ambulatory behavior in this small cohort. Determining functional level based on real world ambulatory activity may supplement clinic-based tests of functional capacity.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.615
Threshold uncertainty score0.288

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 integrity0.0000.001
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 head0.046
GPT teacher head0.313
Teacher spread0.267 · 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

Citations38
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Rehabilitation and Assistive Technologies EngineeringSame topicMuscle activation and electromyography studiesFrench-language works237,207