Pediatric Performance-Based Outcome Measures for Upper Extremity Function: A Scoping Review and Linking to the International Classification of Functioning, Disability, and Health
Bibliographic record
Abstract
Background. Performance-based outcome measures (PBOMs) are objective measures that assess physical capacity or performance in specific tasks or movements. Purpose. 1) to identify which PBOMs are most frequently reported to evaluate upper extremity (UE) function in pediatric rehabilitation 2) to determine the link between constructs of the ICF and meaningful concepts extracted from each identified PBOM. Methods. Pediatric UE PBOMs were searched in four databases. The selection of outcome measures included an initial title and abstract screening, followed by full-text review of the articles to be included based on identified selection criteria. Two reviewers were appointed to link the meaningful concepts identified in the outcome measures independently and a third reviewer was consulted in case of ambiguity to make a final decision. Findings. After the initial screening, 1786 full-text articles were reviewed, 1191 met the inclusion criteria, in which 77 outcome measures were identified and 32 were included in the linking process. From the included 32 outcome measures, 538 items were extracted and linked to the ICF. The most commonly cited measures included Assisting Hand Assessment, Jebsen-Taylor Hand Function Test, Melbourne Assessment of Unilateral Upper Limb. The Activity and Participation domain represented 364 codes followed by the Body Functions domain domain which represented 174 codes. Implications. A majority of the outcome measures identified were linked with the Mobility, Fine Hand Use of the ICF. Therefore, when selecting a PBOM, careful considerations need to be made regarding which concept of health is to be assessed.
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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 | 0.039 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.048 | 0.048 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".