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Record W2160146304 · doi:10.3109/09593985.2013.773574

The International Classification of Functioning, Disability and Health (ICF) Core Sets: Application to a postmenopausal woman with rheumatoid arthritis and osteoporosis of the spine

2013· article· en· W2160146304 on OpenAlexafffund
Amanda L. Lorbergs, Norma J. MacIntyre

Bibliographic record

VenuePhysiotherapy Theory and Practice · 2013
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster University
FundersCanadian Institutes of Health Research
KeywordsInternational Classification of Functioning, Disability and HealthMedicinePhysical therapyOsteoporosisEvidence-based practiceMultidisciplinary approachRheumatoid arthritisCore (optical fiber)Physical medicine and rehabilitationAlternative medicineRehabilitationPathology

Abstract

fetched live from OpenAlex

The International Classification of Functioning, Disability and Health (ICF) framework facilitates systematic assessment of functioning across four components. ICF Core Sets are proposed to be beneficial for clinicians in multidisciplinary care settings because they provide a common language for communication. A clinical vignette of a postmenopausal woman with rheumatoid arthritis (RA) and a non-traumatic vertebral fracture is presented to discuss how the ICF Core Sets for RA and osteoporosis (OP) can be helpful in structuring clinical decisions. To demonstrate how condition-specific ICF Core Sets can be used to evaluate and treat women with two comorbidities, each component of the ICF Core Sets is compared across conditions and integrated into clinical decision-making. Topics covered include: exercise tolerance, urinary continence, bone mass, fear of falling, and environmental factors. The benefits of thorough communication with the client and a common language across healthcare disciplines are highlighted as the potential benefits of the ICF framework; however, limitations to uptake of the ICF in clinical practice are also addressed.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.910
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.318
Teacher spread0.302 · 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

Citations7
Published2013
Admission routes2
Has abstractyes

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