Effectiveness and cost-effectiveness of an optimized process of providing assistive technology for impaired upper extremity function: Protocol of a prospective, quasi-experimental non-randomized study (OMARM)
Bibliographic record
Abstract
BACKGROUND: Impaired upper extremity function due to muscle paresis or paralysis has a major impact on independent living and quality of life (QoL). Assistive technology (AT) for upper extremity function (i.e. dynamic arm supports and robotic arms) can increase a client’s independence. Previous studies revealed that clients often use AT not to their full potential, due to suboptimal provision of these devices in usual care. OBJECTIVE: To optimize the process of providing AT for impaired upper extremity function and to evaluate its (cost-) effectiveness compared with care as usual. METHODS: Development of a protocol to guide the AT provision process in an optimized way according to generic Dutch guidelines; a quasi-experimental study with non-randomized, consecutive inclusion of a control group ( n = 48) receiving care as usual and of an intervention group (optimized provision process) ( n = 48); and a cost-effectiveness and cost-utility analysis from societal perspective will be performed. The primary outcome is clients’ satisfaction with the AT and related services, measured with the Quebec User Evaluation of Satisfaction with AT (Dutch version; D-QUEST). Secondary outcomes comprise complaints of the upper extremity, restrictions in activities, QoL, medical consumption and societal cost. Measurements are taken at baseline and at 3, 6 and 9 months follow-up.
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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 | 0.034 | 0.034 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".