An International Classification of Functioning, Disability, and Health (ICF) comprehensive core set for vertebral fragility fracture
Bibliographic record
Abstract
Purpose To develop a comprehensive ICF Core Set (ICF-CS) for vertebral fragility fracture.Materials and Methods The development of ICF-CSs involves three phases: i) systematic literature review and qualitative studies; ii) linking process to identify the ICF codes and categories; iii) international consensus process. i) We performed a literature search and qualitative studies with people with vertebral fragility fractures and healthcare professionals; ii) We linked the findings from the search and qualitative studies to the ICF categories, and drafted the proposed ICF-CS; iii) We performed an international consensus process involving experts with clinical or research experience in management of vertebral fragility fractures.Results We identified 40 categories (second level categories, n = 28; third level categories, n = 11; and fourth level category, n = 1) from the international consensus process. Sixteen categories pertained to body functions and structures impairments, n = 12 to activity limitations, n = 4 to participations restrictions, and n = 7 to environmental factors.Conclusions The new ICF-CS for vertebral fragility fracture reports the consequences of vertebral fragility fractures in terms of body impairments, activity limitations, and participation restrictions, while taking into account personal and environmental factors that increase the negative impact of vertebral fragility fractures and hinders the access to care.
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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 | 0.028 | 0.082 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.022 | 0.012 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".