Functional Outcomes in the WHO-ICF Model: Establishment of the Upper Limb Prosthetic Outcome Measures Group
Bibliographic record
Abstract
In Brief A need for a systematic measurement of function for upper limb prosthetics has been identified. Using the World Health Organization-International Classification of Functioning, Disability and Health (WHO-ICF) model, it was clear that no single test was able to cover the entire cycle of prosthetic use from research to application in the field, but it was believed that a unified approach throughout the profession would allow better communication between the contributors to this process. Through a series of meetings, such an approach has been formulated and a special interest group formed that aims to analyze the current literature on the subject, and identify which tools already in existence have the psychometric properties that allow for valid comparison of data between centers and countries. After this analysis, recommendations for a toolkit of different validated tools will be made, along with identifying any gaps within the kit that need additional attention. A need for a systematic measurement of function for upper limb prosthetics has been identified. The Upper Limb Prosthetic Outcome Measures (ULPOM) group has begun to work towards this goal through an analysis of the existing literature and through promotion of the ideas to encourage a consensus within the profession. The initial analysis work has begun. Following this analysis, recommendations for a toolkit of different validated tools will be made, along with identifying any gaps within the kit that need additional attention.
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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 | 0.050 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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".