Three-dimensional printed prosthesis demonstrates functional improvement in a patient with an amputated thumb
Bibliographic record
Abstract
BACKGROUND AND AIM: Three-dimensional printer is widely used in industry, biology, and medical fields. We report a finger prosthesis produced by a three-dimensional scanner and printer for a 67-year-old man with a right thumb amputation above the metacarpophalangeal joint. TECHNIQUE: His right amputated and left intact hands were scanned with a three-dimensional scanner, and the left-hand image was rotated to the right side to design the right thumb prosthesis. The designed prosthesis was printed with a three-dimensional printer using the fused filament fabrication output system. DISCUSSION: The Jebsen-Taylor hand function test and Box and Block Test scores improved after application of the prosthesis. Most Quebec User Evaluation of Satisfaction with Assistive Technology results were "very satisfied," and most Orthotics and Prosthetics Users' Survey results were "very easy." Preparing the prosthesis made by three-dimensional scanner and three-dimensional printer was faster and cheaper than preparing a conventional prosthesis. Clinical relevance Using three-dimensional scanning and printing technique, we can easily produce specifically shaped finger prostheses for specific movements in amputated patients with low cost.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".