The International Classification of Functioning, Disability and Health (ICF) Core Sets: Application to a postmenopausal woman with rheumatoid arthritis and osteoporosis of the spine
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".