Making Safe Surgery Affordable
Bibliographic record
Abstract
Many surgeons in low-resource settings do not have access to safe, affordable, or reliable surgical drilling tools. Surgeons often resort to nonsterile hardware drills because they are affordable, robust, and efficient, but they are impossible to sterilize using steam. A promising alternative is to use a Drill Cover system (a sterilizable fabric bag plus surgical chuck adapter) so that a nonsterile hardware drill can be used safely for surgical bone drilling. Our objective was to design a safe, effective, affordable Drill Cover system for scale in low-resource settings. We designed our device based on feedback from users at Mulago Hospital (Kampala, Uganda) and focused on 3 main aspects. First, the design included a sealed barrier between the surgical field and hardware drill that withstands pressurized fluid. Second, the selected hardware drill had a maximum speed of 1050 rpm to match common surgical drills and reduce risk of necrosis. Third, the fabric cover was optimized for ease of assembly while maintaining a sterile technique. Furthermore, with the Drill Cover approach, multiple Drill Covers can be provided with a single battery-powered drill in a "kit," so that the drill can be used in back-to-back surgeries without requiring immediate sterilization. The Drill Cover design presented here provides a proof-of-concept for a product that can be commercialized, produced at scale, and used in low-resource settings globally to improve access to safe surgery.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".