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Record W2168892403 · doi:10.2340/16501977-1784

Comparison of generic patient-reported outcome measures used with upper extremity musculoskeletal disorders: Linking process using the International Classification of Functioning, Disability, and Health (ICF)

2014· review· en· W2168892403 on OpenAlexaff
Nancy Forget, Johanne Higgins

Bibliographic record

VenueJournal of Rehabilitation Medicine · 2014
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsInternational Classification of Functioning, Disability and HealthSet (abstract data type)Patient-reported outcomeCore (optical fiber)Process (computing)PsychologyOutcome (game theory)MEDLINEIdentification (biology)Physical therapyPhysical medicine and rehabilitationApplied psychologyMedicineComputer scienceQuality of life (healthcare)RehabilitationPsychotherapistMathematics

Abstract

fetched live from OpenAlex

OBJECTIVE: To report the theoretical foundation of generic patient-reported outcomes for measuring functioning related to upper extremity musculoskeletal disorders and perform content coverage analysis and content comparison using the International Classification of Functioning, Disability and Health (ICF). METHODS: A literature search was performed to identify commonly used patient-reported outcomes. A comparison of their theoretical foundations and a linking exercise between the measures' meaningful concepts and the ICF and Brief ICF Core Set for Hand Conditions was accomplished based on established rules. RESULTS: Fifteen measures were selected. Multiple theoretical foundations were identified, and only 7 measures were developed based on a known conceptual model. Six measures were chosen for the linking process with 232 meaningful concepts retrieved and linked to 54 ICF categories. No concept was linked to the Body Structures component and two measures stood out for their Activity and Participation coverage. No measure covered all Brief ICF Core Set for Hand Conditions recommended categories. CONCLUSION: Some heterogeneity was observed with regards to the theoretical foundations on which the identified measures are based. The results of the linking process should help reduce these inconsistencies. They enable easy identification of content coverage and content comparison between measures using a common framework and can be used as a reference when selecting the most appropriate patient-reported outcome measure.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.409
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.149
GPT teacher head0.440
Teacher spread0.291 · 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
Published2014
Admission routes1
Has abstractyes

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