The roles of predisposing characteristics, established need, and enabling resources on upper extremity prosthesis use and abandonment
Bibliographic record
Abstract
PURPOSE: Prosthesis use and abandonment is a complex function of variables defining the contextualized individual. This review presents a comprehensive panoramic of these factors as related to the management of upper limb deficiency. Me METHOD: nderson's model for health service utilization was used to frame prosthesis use and abandonment as a function of (1) predisposing characteristics of the individual (e.g. gender or level of limb loss); (2) established need, as characterized by lifestyle- and age-related demands; and (3) enabling resources (e.g. clinical and social). English-language articles pertaining to these components were identified in a search of Ovid, PubMed, ISI Web of Science and www.scholar.google.com (1980-November 2006) for key words upper limb and prosthesis. Approximately 90 articles were included as evidence in this review. Re RESULTS: ersonal and contextual factors are critical determinants of prosthesis acceptance. While the influence of some factors (i.e. lifestyle, level of limb loss), is strongly supported in the literature, the impact of others, (i.e. age of fitting, efficacy of training protocols), remain controversial. Co CONCLUSIONS: nhanced understanding of these factors is required to optimize clinical practices, guide design efforts, and satiate demand for evidence-based measures of intervention. Future research should comprise of controlled, multifactor studies adopting standardized outcome measures and providing comprehensive descriptions of population characteristics.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".