Clinical outcomes measurement in pediatric lower limb prosthetics: A scoping review
Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify clinical measures that have been used to evaluate function, health related quality of life (HRQoL), and/or satisfaction in children who use lower limb prostheses (LLP). The data reported on psychometric properties for children who use LLP were collected for each measure. METHODS: First, PubMed, CINAHL, and Web of Science databases were searched using broad search terms to identify standardized outcome measures of function, HRQoL, and/or satisfaction with treatment used in pediatric LLP research published in 2001 or after. For each of the eligible measures found, a second search was performed to identify psychometric properties (e.g., validity, reliability) assessed with children who use LLP. RESULTS: Forty-four standardized outcome measures were identified from 41 pediatric LLP research articles. Five measures (i.e., Gait Outcomes Assessment for Lower Limb Differences, Functional Mobility Assessment, Child Amputee Prosthetics Project- Prosthesis Satisfaction Inventory, Child Amputee Prosthetics Project- Functional Scale Index, and Lower Limb Function Questionnaire) had data on psychometric properties for children who use LLP. CONCLUSIONS: Few studies report psychometric data for assessing the overall HRQoL, function, and/or satisfaction for children who use LLP. Further research is needed to validate or create new outcome measures that assess the HRQoL, satisfaction, and/or function of children who use LLP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".