Instruments for assessment of impairments and activity limitations in patients with hand conditions: A European Delphi study
Bibliographic record
Abstract
OBJECTIVE: To reach multidisciplinary European consensus on the assessment tools for impairments and activity limitations in patients with hand conditions. DESIGN: Electronic Delphi method. SUBJECTS: Thirty experts from European societies for hand therapy, hand surgery, and physical and rehabilitation medicine. METHODS: In 3 rounds, participants were asked which of 13 preselected categories of the Brief International Classification of Functioning, Disability and Health (ICF) Core Set for Hand Conditions should be assessed. In addition, they were asked to choose which of 55 preselected instruments they preferred for each category by confirming or rejecting instrument-specific statements. RESULTS: All 13 preselected ICF categories were considered relevant. Consensus was based on ≥ 75% agreement. After 3 rounds, 9 instruments were selected: Shape Texture Identification Test, Semmes Weinstein Monofilament Test, Visual Analogue Scale for pain, goniometer, Jamar Dynamometer, Pinch Gauge Device, Cold Intolerance Symptom Severity questionnaire, Canadian Occupational Performance Measure, and Disabilities of the Arm, Shoulder and Hand Questionnaire. It remained undecided whether to use the Nine-Hole Pegboard Test or the Purdue Pegboard Test. CONCLUSION: In this European Delphi study, multidisciplinary consensus was reached on 9 assessment tools for impairments and activity limitations in patients with hand conditions addressing 13 categories of the Brief ICF Core Set for Hand Conditions.
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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 | 0.061 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".