Stakeholder Engagement in the Development of an Upper Extremity Outcome Measure for Children with Rare Musculoskeletal Conditions
Bibliographic record
Abstract
Abstract Background Individuals with arthrogryposis multiplex congenita (AMC) have upper extremity (UE) involvement in 73% of cases, yet no AMC specific outcome measure exists. When developing a measure specific to a population with a rare musculoskeletal condition, clinicians’ and patients’ perspectives and involvement is a crucial and necessary step. Therefore, this study determines the most clinically useful items for an outcome measures of UE function for children with AMC as defined by caregivers and clinicians.Methods To ensure the perspectives and needs of caregivers of children with AMC and clinicians were considered in the development of the UE measure for AMC, a Nominal Group technique (NGT) with caregivers of children with AMC (phase 1) followed by a three-round survey with clinicians (phase 2) were carried out.Results Phase 1: Eleven individuals participated in the nominal group technique and identified 32 items. The most important items were Picking up an object (n = 11), Eating (n = 10), Reaching mouth (n = 10), Getting out of bed (n = 10). Phase 2: Invitations to participate to an online survey was sent to 47 experts in the field of AMC, 20 participants completed round 1, 15 completed round 2 and 13 completed round 3. Throughout the survey, participants were asked about movement required to screen the UE, essential domains to be included in the measure, establishing a scoring guide and identifying tasks associated with joint motion and position.Conclusion A preliminary version of an UE AMC-specific outcome measure was developed with the help of caregivers’ perspectives and expert opinions.
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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 | 0.033 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".