User expectations and experiences of an assistive robotic arm in amyotrophic lateral sclerosis: a multicenter observational study
Bibliographic record
Abstract
OBJECTIVE: Robotic arms are innovative assistive devices for ALS patients with progressive motor deficits of arms and hands. The objective was to explore the patients´ expectations towards a robotic arm system and to assess the actual experiences after the provision of the device. METHODS: A prospective observational study was conducted at 9 ALS centers in Germany. ALS-related functional deficits were assessed using the ALS-Functional Rating Scale-revised (ALSFRS-R). Motor deficit of the upper limbs was determined using a subscore of three arm-related items of the ALSFRS-R (items 4-6; range 0-12 points). User expectations before provision (expectation group, n = 85) and user experiences after provision (experience group, n = 14) with the device (JACO Assistive Robotic Device, Kinova, Boisbriand, QC, Canada) were assessed. RESULTS: In the total cohort, mean ALSFRS-R subscore for arm function was 1.7 (SD: 2.0, 0-9) demonstrating a severe functional deficit of the upper limbs. In the expectation group (n = 85), the following use cases of the robotic arm have been prioritized: handling objects (89%), close-body movements (88%), pressing buttons (87%), serving drinks (86%), and opening cabinets and doors (85%). In the experience group (n = 14), handling objects (79%), serving drinks (79%), near-body movements (71%), pushing buttons (71%), serving food (64%), and opening doors (64%) were the most frequent used cases. Most patients used the device daily (71.4%, n = 10), and 28.6% (n = 4) several times a week. All patients of the experience group found the device helpful, felt safe while using the device, and were satisfied with its reliability. NPS of the assistive robotic arm revealed 64% "promoters" (strong recommendation), 29% "indifferents" (uncertain recommendation) and 7% "detractors" (no recommendation). Total NPS was + 57 demonstrating strong patient satisfaction. CONCLUSIONS: Initiation of procurement with a robotic assistive arm was confined to patients with severe functional deficit of the upper limbs. User experience underlined the wide spectrum of use cases of assistive robotic arms in ALS. The positive user experience together with high satisfaction underscore that robotic arm systems serve as a valuable treatment option in ALS patients with severe motor deficits of the arms.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".