Gross motor functional classification for arthrogryposis multiplex congenita: protocol for co-development involving public with lived and professional experience
Bibliographic record
Abstract
BACKGROUND: Children with arthrogryposis multiplex congenita (AMC), a group of over 400 conditions characterized by congenital joint contractures, present with a wide range of gross motor functioning and mobility due to the heterogeneity of underlying diagnoses and physical involvement. Existing classification systems for AMC focus primarily on etiology and anatomical distribution but do not describe differences in gross motor functioning. In contrast, condition-specific functional classification systems have improved communication, treatment planning, and research by stratifying individuals based on functional ability. To date, no such classification system exists for AMC. This study aims to co-develop and evaluate the Gross Motor Functional Classification for Arthrogryposis Multiplex Congenita (GMFC-AMC), a condition-specific tool to support clinical and research needs. METHODS: This multi-phase study involves stakeholders with lived and professional experience in AMC from multiple countries. Phase 1 includes the initial drafting of the GMFC-AMC by an expert panel. In Phase 2, nominal group techniques are used to refine the draft. Phase 3 employs Delphi surveys to achieve international consensus on relevance, clarity, and comprehensibility. In Phase 4, the GMFC-AMC is translated and culturally adapted into French and Spanish following a structured cross-cultural adaptation process. Finally, Phase 5 evaluates reliability and construct validity across three languages. Inter-rater and test-retest reliability will be examined using video ratings of children with AMC. Construct validity will be assessed through correlations with established measures, including the Gillette Functional Assessment Questionnaire and the Pediatric Evaluation of Disability Inventory – Computer Adaptive Test. DISCUSSION: The GMFC-AMC will address a critical gap in functional classification for children and youth with AMC by providing a standardized language for describing gross motor functioning. By engaging international stakeholders throughout the development process and ensuring rigorous testing of its psychometric properties, the GMFC-AMC is expected to be a reliable, valid, and meaningful tool across diverse clinical and cultural contexts. This classification will facilitate clearer communication, improve care planning, and enable stratification for future longitudinal and interventional studies in AMC populations. TRIAL REGISTRATION: Clinical trial number not applicable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".