Open source surgical fracture table for digitally distributed manufacturing
Bibliographic record
Abstract
Roughly a third of the surgical procedures the World Bank is prioritizing as essential and cost-effective are orthopedic procedures. Yet in much of the developing world, prohibitive costs are a substantial barrier to universal access. One area where this is clear is surgical fracture tables, which generally cost >US$200,000 new. With the advent of 3-D printing, a new way to reduce medical equipment costs is to use open source hardware licensed designs to fabricate digitally-distributed manufactured medical hardware. That approach is applied here to make surgical tables more accessible. This study describes the design and manufacture of an open source surgical fracture table that uses materials that are widely available worldwide with specialty components being 3-D printed. The bill of materials and assembly instructions are detailed and the fracture table is validated to perform mechanically to specifications. Using an open source desktop RepRap-class 3-D printer, the components can be printed in a little over a week of continuous printing. Including the 3-D printed parts, the open source fracture table can be constructed for under US$3,000 in material costs, representing a 98.5% savings for commercial systems, radically increasing accessibility. The open source table can be adjusted 90-116 cm in height, tilted from +/-15 degrees, the leg height ranges from 31 to 117 cm, the arm supports and foot holder both have a 180-degree range, the foot position has a 54 cm range, and the legs can be adjusted from 55 to 120 degrees. It is mechanically adjusted so does not require electricity, however, surgical staff need to be trained on how to perform needed adjustments during surgery. The open source surgical table has verified performance for mechanical loading over 130 kg, geometric flexibility to allow for wide array of common surgeries, is radiolucent in surgical zones, and is modular and upgradeable.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.290 | 0.123 |
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".