An ICF-based education programme in amputation rehabilitation for medical residents in the Netherlands
Bibliographic record
Abstract
BACKGROUND AND AIM: Education programmes of the International Society for Prosthetics and Orthotics (ISPO) are directed primarily at prosthetists and orthotists. In a multidisciplinary setting, greater attention should be given to other professionals working in the field of amputation, prosthetics and orthotics. This includes, among others, physiotherapists, occupational therapists and residents and physicians from orthopaedics, vascular surgery and physical medicine and rehabilitation (PM&R). The aim of this paper is to describe the education programme in amputation and prosthetics for residents in PM&R in the Netherlands. The programme is based on concepts of the International Classification on Functioning, Disability and Health (ICF). TECHNIQUE: This narrative paper presents the amputation and prosthetics education programme for residents in PM&R in the Netherlands. The programme is based on two models: the ICF and the Canadian Medical Education Directives for Specialists (CanMEDS). DISCUSSION: ICF core sets for amputation and prosthetics need further development. Subsequently, the application of these core sets can help stimulate the education of residents in PM&R, and other multidisciplinary team members who work in amputation rehabilitation. CLINICAL RELEVANCE: Through this education programme, residents work closely with other team members using the common language of the ICF, enhancing treatment and technical advice in amputee care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".