The 3D printed ring-based finger splint - a cheap and lightweight alternative
Bibliographic record
Abstract
Introduction and purpose The orthosis is used to assist the function of the injured limb or to stop or limit the movement during the healing process. The actual process of making the orthosis is quite time-consuming [1]. An addictive manufacturing process, known commonly as 3D printing can be advantageous in creating cheap and highly customizable prosthetics. Using Fusion Deposition Modeling (FDM), where each layer of material is deposited right on the previous one is now fully available both in professional and consumer-grade printers. The main aim of this study was to create an easily customizable and cheap 3D printable finger splint with the use of Fusion Deposition Modeling technology. Material and methods BambuLab P1P 3D printer with CoreXY kinematics was used. The filament used was the 1.75mm PLA (polylactic acid). OnShape was used as CAD software. Results Basing our project on the three rings, which dimensions can be easily measured with a set of calipers or basic measuring tape we were able to develop a fully customizable finger splint that weighed less than 10 grams and could be fully prepared within 1 hour, including taking measurements, modifying the 3D model and printing it. Due to the fact, that PLA starts to deform around 60-65 degrees Celsius, with the use of hot water we could thermoform the splint after the printing, providing an even more precise fit with the “injured” finger. Modifying every measurement and aspect of the splint is simple due to the use of parametric design rules. Conclusions We were able to create a cheap splint, easy to print, and highly customizable to fit as many different patients as possible. In our opinion, 3D printing is a promising technology. With the lowering cost of equipment and filament, one day it might be a viable option in the process of creating individualized orthosis on a mass scale.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".