Error Traps in Global Anesthesia
Bibliographic record
Abstract
Anesthesia is increasingly acknowledged as a neglected priority in global health, and pediatric anesthesia is especially important due to the high proportion of children in the least developed countries with a large unmet burden of surgical disease. Pediatric anesthesiologists involved in global health may encounter several common "error traps" that could either lead to missed opportunities to build on recent advancements in global anesthesia or potentially cause harm. We present a number of these "traps" based on the literature and our experience from both sides of global health partnerships in East and Southern Africa, India, and the Caribbean. These error traps include failing to appreciate the public health "big picture"; failing to consider a health-systems approach, prioritizing quantity-based outcomes at the expense of quality, having priorities driven by partners in the "Global North"; failing to make programs sustainable, failing to invest in the retention of anesthesia providers, not realizing that not all global health is international health, and unethical practices. Our goal is to spark debate on ongoing controversies and to inform pediatric anesthesiologists who are working or considering a career in this field.
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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 | 0.108 | 0.214 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.033 |
| Scholarly communication | 0.012 | 0.020 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.006 | 0.016 |
| Insufficient payload (model declined to judge) | 0.011 | 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".