MétaCan
Menu
Back to cohort
Record W1999019264 · doi:10.1080/17483100601138959

The roles of predisposing characteristics, established need, and enabling resources on upper extremity prosthesis use and abandonment

2007· review· en· W1999019264 on OpenAlex
Elaine Biddiss, Tom Chau

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueDisability and Rehabilitation Assistive Technology · 2007
Typereview
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsUniversity of TorontoHolland Bloorview Kids Rehabilitation Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAbandonment (legal)ProsthesisBusinessMedicinePhysical medicine and rehabilitationPolitical scienceSurgery

Abstract

fetched live from OpenAlex

PURPOSE: Prosthesis use and abandonment is a complex function of variables defining the contextualized individual. This review presents a comprehensive panoramic of these factors as related to the management of upper limb deficiency. Me METHOD: nderson's model for health service utilization was used to frame prosthesis use and abandonment as a function of (1) predisposing characteristics of the individual (e.g. gender or level of limb loss); (2) established need, as characterized by lifestyle- and age-related demands; and (3) enabling resources (e.g. clinical and social). English-language articles pertaining to these components were identified in a search of Ovid, PubMed, ISI Web of Science and www.scholar.google.com (1980-November 2006) for key words upper limb and prosthesis. Approximately 90 articles were included as evidence in this review. Re RESULTS: ersonal and contextual factors are critical determinants of prosthesis acceptance. While the influence of some factors (i.e. lifestyle, level of limb loss), is strongly supported in the literature, the impact of others, (i.e. age of fitting, efficacy of training protocols), remain controversial. Co CONCLUSIONS: nhanced understanding of these factors is required to optimize clinical practices, guide design efforts, and satiate demand for evidence-based measures of intervention. Future research should comprise of controlled, multifactor studies adopting standardized outcome measures and providing comprehensive descriptions of population characteristics.

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.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.926
Threshold uncertainty score0.923

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.016
GPT teacher head0.260
Teacher spread0.244 · 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