Qualitative & Quantitative Assessment of 3D Printing of Prostheses in Low Economic Setting
Bibliographic record
Abstract
The applicability and cost effectiveness in prosthetic industry is monumental. Underdeveloped parts of the World are coping with challenges like poverty, lack of technology & expertise, natural & man-made disasters as well as conundrums of untapped explosive mines. In addition, people living in such areas are mainly associated with labour intensive professions working in small industries and agriculture lacking health & safety measures. Individuals exposed to such environment are often prone to accidents due to safety lapses & fatal diseases and need low-cost prosthetic devices due to amputation in order to rehabilitate, get back to work and live a better quality of life. The consideration of supply chain models, technology used in additive manufacturing, materials properties, cost & customer satisfaction is essential for optimizing the use of 3D printing & additive manufacturing in prosthesis in such parts of the World. The usage of novel techniques such as 3D Printing and conventional methods like Injection Molding and feasibility of providing optimum solution in terms of hybrid models while considering the advantages of availability, affordability and mass production of conventional techniques, and prototype development, simulation, customization and design freedom using 3D printing & additive manufacturing is qualitatively & quantitatively analyzed in this paper.
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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".