Assessment of 3D Printing for Surgical Instrument Manufacturing
Bibliographic record
Abstract
Three-dimensional (3D) printing has emerged as a method for rapid prototyping and manufacturing of tools.In low-resource settings or field settings, the ability to perform surgeries is often limited by a lack of surgical instruments.On-demand manufacture of surgical instruments via 3D printing may offer a low-cost, reliable, convenient solution for provision of necessary care, particularly during trauma or emergency situations.The global coronavirus-19 disease pandemic has emphasized the need for rapid manufacturing of surgical instruments at the point of care, as the pandemic has often limited patient access to hospitals, due to measures to minimize the spread of infectious disease.Moreover, the ability to 3D print surgical instruments is a priority for enabling surgery during space missions.Recent progress has been made on 3D printing of commonly used surgical instruments from plastics.Important surgical tools such as forceps, scalpel handles, needle drivers, Army/Navy retractors, and hemostats have all been 3D printed, with typical print times on the order of hours.This paper assesses the current status of 3D printing of surgical instruments.The review will include 3D printing methods, raw materials, design times, print times, sterilization methods, and the types of surgical instruments that have been successfully printed.In addition, the results of mechanical testing and simulated surgical testing of 3D printed surgical instruments will be described.Finally, avenues for future work will be identified, including the need for faster print times, and the necessity for producing more intricate instruments via 3D printing.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".