Bibliographic record
Abstract
ABSTRACT The purpose of this article is to give an overview on the perspective of different health care systems around the world on the coverage of active upper-limb prostheses for individuals with an upper-limb amputation. Because no authoritative statistics are publicly available, data were collected by interviewing acknowledged professionals in the field of upper-limb prosthetic rehabilitation as well as by obtaining estimates of market sizes from Ottobock's business unit, national market managers, and clinical prosthetists specialized in upper-limb prosthetics in various countries. Countries may be placed in one of three categories upon the basic approach to the provision of active upper-limb prostheses: 1. Industrialized countries with health care systems that grant access to all types of active upper-limb prostheses (e.g., Western and Northern Europe). In these countries, myoelectric/externally powered upper-limb prostheses are considered standard of care. However, adoption levels vary depending on differences in coverage policies. In countries with health care systems that cover several upper-limb prostheses at a time, 80% to 90% of patients use myoelectrics as their primary prosthesis. If the health care system covers only one prosthesis at a time, the proportion of myoelectric prostheses may decline to 50% to 60%. 2. Industrialized countries with health care systems that limit access to active upper-limb prostheses primarily to body-powered devices (e.g., United States, Canada, Australia, New Zealand, Japan). In these countries, myoelectric/externally powered prostheses require approved exceptions from coverage policies and are typically used by less than 35% to 40% of all patients with active prostheses. However, in some specialized urban rehabilitation clinics, the share of myoelectric prostheses may reach up to 70%. 3. Countries with health care systems that provide passive or no upper-limb prostheses to the majority of their beneficiaries (e.g., Eastern Europe, Latin America, Asia, Africa). Although the scientific evidence for upper-limb prosthetics is the same around the world, coverage policies and funding vary remarkably and result in strikingly different adoption rates of active upper-limb prosthetic technologies among different countries. Among industrialized countries, the most important difference seems to be whether policies only consider prosthetic function or also psychosocial aspects for determining medical necessity of the available active prosthetic technologies and designs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".