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Record W2020865823 · doi:10.1080/09638280400013990

ICF Core Set for patients with musculoskeletal conditions in the acute hospital

2005· review· en· W2020865823 on OpenAlexfundno aff
Thomas Stoll, Mirjam Brach, Erika Omega Huber, M Scheuringer, S. R. Schwarzkopf, Nenad Konstanjsek, Gerold Stucki

Bibliographic record

VenueDisability and Rehabilitation · 2005
Typereview
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsnot available
FundersMcMaster University
KeywordsInternational Classification of Functioning, Disability and HealthSet (abstract data type)Core (optical fiber)MedicinePhysical therapyComponent (thermodynamics)MEDLINEMinimum Data SetFamily medicineRehabilitationComputer scienceNursing

Abstract

fetched live from OpenAlex

PURPOSE: The aim of this consensus process was to decide on a first version of the ICF Core Set for patients with musculoskeletal conditions in the acute hospital. METHODS: The ICF Core Set development involved a formal decision-making and consensus process integrating evidence gathered from preliminary studies including focus groups of health professionals, a systematic review of the literature and empiric data collection from patients. RESULTS: Twenty-one experts selected a total of 47 second-level ICF categories. The largest number of categories was selected from the ICF component Body Functions (17 categories or 36%). Nine (19%) of the categories were selected from the component Body Structures, 11 (23%) from the component Activities and Participation, and 10 (21%) from the component Environmental Factors. CONCLUSION: The Acute ICF Core Set for patients with musculoskeletal conditions provides all professionals with a clinical framework to comprehensively assess patients in the acute hospital. This first ICF Core Set will be further tested through empiric studies in German-speaking countries and internationally.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.847
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.018
GPT teacher head0.356
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 teacher head, not a consensus.

Study designOther design
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

Citations66
Published2005
Admission routes1
Has abstractyes

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