Survey of upper limb prosthesis users in Sweden, the United Kingdom and Canada
Bibliographic record
Abstract
BACKGROUND: As part of the process of improving prosthetic arms, it is important to obtain the opinions of the user population. OBJECTIVES: To identify factors that should be focused on to improve prosthesis provision. STUDY DESIGN: Postal questionnaire. METHODS: The questionnaire was sent to 292 adults (aged 18 to 70 years) with upper-limb loss or absence at five centres (four in Europe) Participants were identified as regular attendees of the centres. RESULTS: This questionnaire received a response from 180 users (response rate 62%) of different types of prosthetic devices. Responses showed that the type of prosthesis generally used was associated with gender, level of loss and use for work (Pearson chi-square, p-values below 0.05). The type of prosthesis was not associated with cause, side, usage (length per day, sports or driving) or reported problems. The findings did not identify any single factor requiring focus for the improvement of prostheses or prosthetic provision. CONCLUSIONS: Every part of the process of fitting a prosthesis can be improved, which will have an effect for some of the population who use their devices regularly. There is, however, no single factor that would bring greater improvement to all users. CLINICAL RELEVANCE: Based on information gained from a broad range of prosthesis users, no single aspect of prosthetic provision will have a greater impact on the use of upper limb prostheses than any other. Efforts to improve the designs of prosthetic systems can cover any aspect of provision.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".