The Capnography Project
Bibliographic record
Abstract
Capnography is an essential tool used in the monitoring of patients during anesthesia and in critical care which, while required in most high-income countries, is unavailable in many low- and middle-income countries. Launched in 2020, the Smile Train-Lifebox Capnography Project aimed to find a "capnography solution" for resource-poor settings. The project was specifically interested in a capnography device that would meet the needs of the Smile Train partner hospitals to help monitor children requiring airway or cleft surgery. Project advisory and technical groups were formed and included representation from anesthesia practitioners from a balanced representation from all level of income countries, technical experts in capnography, and representatives from the Global Capnography Project (GCAP), the University of California at San Francisco Center for Health Equity in Surgery & Anesthesia (CHESA), and the World Federation of Societies of Anaesthesiologists (WFSA). Built upon the WFSA minimum capnometer specifications, a human centered design approach was used to develop a Target Product Profile. Seven manufacturers submitted 13 devices for consideration and 3 devices were selected for the testing phase. Each of these devices was evaluated for build quality, and clinical and usability performance. Based on the findings from the overall testing process, a combined capnography and pulse oximetry device by Zug Medical Systems was chosen. To accompany the new Smile Train-Lifebox capnograph, an international team of experienced anesthesiologists and educators came together to develop the necessary education materials. These materials were piloted in Ethiopia, subsequently modified, and endorsed by the education team. The device is now ready for distribution, with the accompanying education package, to the Smile Train network and beyond. In addition, a study is being planned to measure the impact of capnography introduction into operating rooms in resource-constrained settings.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.163 | 0.083 |
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".