Development of the gait outcomes assessment list for lower-limb differences (GOAL-LD) questionnaire: a child and parent reported outcome measure
Bibliographic record
Abstract
BACKGROUND: To develop a priority-based patient/parent reported outcome measure for children with lower-limb differences (LD) by adapting the Gait Outcomes Assessment List (GOAL) questionnaire. METHODS: Guided by a conceptual framework of patient priorities, the GOAL questionnaire was iteratively modified and its sensibility evaluated by field-testing it on children with LD, and their parents. Cognitive interviews were conducted with a subgroup of these children, and an e-survey administered to a multidisciplinary group of health care professionals with expertise in paediatric LD. Findings were integrated to create the final version of the GOAL-LD. RESULTS: Twenty-five children (9-18 years), 20 parents, and 31 healthcare professionals evaluated the content and sensibility of the GOAL, with an emphasis on the relevance and importance of the items to patients' health related quality of life (HRQL). This resulted in the retention of 26 of the original 50 items, elimination of 12, modification of 12, and addition of seven new items. The new 45-item GOAL-LD questionnaire was shown to be sensible, and its content deemed important. CONCLUSIONS: The GOAL-LD questionnaire has a high level of face and content validity, and sensibility. It comprehensively captures the HRQL goals and outcomes that matter to children with LD and their parents. Following further psychometric evaluation, the GOAL-LD may serve as a much needed patient and parent reported outcome measure for this population.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".