WALANT Decreases Costs of Surgery to Increase Access and Help Alleviate Poverty in East Africa
Bibliographic record
Abstract
Surgery is essential to help people regain health and get back to work. Many Africans cannot afford traditional surgery because the sedation and main operating room sterility components are much too expensive. This often results in crushing debt for African families. Lack of access to surgery leads to poverty and poverty leads to lack of access to surgery. Wide-awake local anesthesia no tourniquet surgery, minimal pain tumescent local anesthesia, and evidence-based sterility are 3 disruptive game-changing innovations that eliminate the expensive general anesthesia and/or main operating room sterility components for many operations. Eliminating the tourniquet removes its need for sedation. Minimal pain tumescent local anesthesia enables comfortable numbing of large areas of the body to perform sedation-free operations such as soft tissue facial reconstruction, long bone fracture fixation, breast surgery, hernia repair, extremity surgery, and skin grafting. Evidence-based sterility has proven that many operations can be performed with field sterility outside of the main operating room environment with no significant increase in infection rates. No sedation also means no need for the main operating room environment. Moving some surgery out of the main operating room increases access for other operations that need full sterility to be accomplished. Since January 2020, these 3 disruptive changes have been adopted in 75 hospitals in 8 East African countries. This article documents how these changes have decreased the costs of surgery for the patients and, therefore, increased access to surgery, which helps alleviate poverty.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".