Psychometric findings for LIMB-Q kids based on an international study of 800 children and adolescents with lower limb differences
Bibliographic record
Abstract
BACKGROUND: LIMB-Q Kids is a new patient-reported outcome measure (PROM) for children and adolescents with Lower limb differences (LLDs). This international field test study aimed to evaluate the psychometric properties of LIMB-Q Kids. METHODOLOGY: Patients from pediatric orthopaedic clinics with a diagnosis of LLDs were recruited. Participants completed LIMB-Q Kids and 2 generic quality of life questionnaires, i.e., PROMIS Pediatric Short Form v2.0 - Mobility 8a, and PedsQL. Demographic and clinical data were collected including the LLRS AIM Index, a measure of clinical severity for LLDs. Rasch measurement theory (RMT) analysis was used to examine the psychometric properties of LIMB-Q Kids. Test-retest (TRT) reliability was examined and tests of construct validity were performed. RESULTS: Participants (N = 800) were recruited from 16 sites in 7 countries. Participants were aged 8 years and older (mean = 13, standard deviation = 3.2, range 8-25 years) and had a broad range of LLDs (e.g., Leg Length Discrepancy, Fibular Hemimelia, Skeletal Dysplasia, Blount's disease, Posteromedial Tibial Bowing, Osteogenesis Imperfecta, Congenital Pseudarthrosis of Tibia, Tibial Hemimelia and Amputations). RMT analysis provided evidence of the reliability and validity of 9 independently functioning scales that measure leg appearance, physical function, symptoms (hip, leg, knee, ankle, and foot), leg-related distress, and social, and psychological function. In addition, TRT reliability based on a sample of 46 participants was high for all 9 scales (Intraclass correlation coefficient ranges from 0.76-0.95). LIMB-Q Kids Physical Function scale correlated strongly with the PROMIS Pediatric Short Form v2.0 - Mobility 8a (Pearson correlation 0.82) and the PedsQL Physical Function total score (Pearson correlation 0.77). As hypothesized, participants with more severe LLDs based on the LLRS AIM index scores reported lower scores on all LIMB-Q Kids scales, indicating more impact on the patients. CONCLUSIONS: This study provided evidence for the validity and reliability of LIMB-Q Kids. This new PROM can be used to inform research, quality improvement efforts, and clinical care. By measuring outcomes that matter most to children and adolescents with LLDs, LIMB-Q Kids can provide information to support evidence-based decisions. LEVEL OF EVIDENCE: Level III.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".