ICF Core Set for patients with musculoskeletal conditions in the acute hospital
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.098 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.015 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".