The Quality Function Measure: reliability and discriminant validity of a new measure of quality of gross motor movement in ambulatory children with cerebral palsy
Bibliographic record
Abstract
AIM: Optimizing movement quality is a common rehabilitation goal for children with cerebral palsy (CP). The new Quality Function Measure (QFM)--a revision of the Gross Motor Performance Measure (GMPM)--evaluates five attributes: Alignment, Co-ordination, Dissociated movement, Stability, and Weight-shift, for the Gross Motor Function Measure (GMFM) Stand and Walk/Run/Jump items. This study evaluated the reliability and discriminant validity of the QFM. METHOD: Thirty-three children with CP (17 females, 16 males; mean age 8y 11mo, SD 3y 1mo; Gross Motor Function Classification System [GMFCS] levels I [n=17], II [n=7], III [n=9]) participated in reliability testing. Each did a GMFM Stand/Walk assessment, repeated 2 weeks later. Both GMFM assessments were videotaped. A physiotherapist assessor pair independently scored the QFM from an assigned child's GMFM video. GMFM data from 112 children. That is, (GMFCS I [n=38], II [n=27], III [n=47]) were used for discriminant validity evaluation. RESULTS: QFM mean scores varied from 45.0% (SD 27.2; Stability) to 56.2% (SD 27.5; Alignment). Reliability was excellent across all attributes: intraclass correlation coefficients (ICCs) ≥0.97 (95% confidence intervals [CI] 0.95-0.99), interrater ICCs ≥0.89 (95% CI 0.80-0.98), and test-retest ICCs ≥0.90 (95% CI 0.79-0.99). QFM discriminated qualitative attributes of motor function among GMFCS levels (maximum p<0.05). INTERPRETATION: The QFM is reliable and valid, making it possible to assess how well young people with CP move and what areas of function to target to enhance quality of motor control.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.000 |
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".