International Classification of Functioning, Disability and Health Core Set construction in systemic sclerosis and other rheumatic diseases: a EUSTAR initiative
Bibliographic record
Abstract
Objectives. To outline rationale and potential strategies for rheumatology experts to be able to develop disease-specific Core Sets under the framework of the International Classification of Functioning, Disability and Health (ICF). ICF is a universal framework introduced by the World Health Organization (WHO) to describe and quantify the impact and burden on functioning of health conditions associated with impairment/disability. Methods. A combined effort of the EULAR Scleroderma Clinical Trial and Research and the ICF Research Branch was initiated to develop an ICF language for scleroderma. From our Medline literature review, using the abbreviation and spelled out version of ICF, we assembled approaches and methodological reasoning for steps of core set development. Results. The ICF can be used for patient care and policy-making, as well as the provision of resources, services and funding. The ICF is used on institutional, regional, national and global levels. Several diseases now have ICF Core Sets. Patients with complex rheumatologic diseases will benefit from a disease-specific ICF Core Set and should be included in all stages of development. ICF Core Set development for rheumatic diseases can be conducted from a number of feasible strategies. Conclusion. This overview should help to clarify useful processes leading to development of an ICF Core Set, and also provide a platform for expert groups considering such an endeavour.
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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 | 0.068 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.021 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".