Capnography for Monitoring End-Tidal CO2 in Hospital and Pre-hospital Settings: A Health Technology Assessment
Bibliographic record
Abstract
Anesthesiologists have been using capnography for decades to monitor end-tidal carbon dioxide (ETCO2) in patients receiving general anesthesia. ETCO2 monitoring using capnography devices has application across several hospital and pre-hospital settings, including monitoring the effectiveness of cardiopulmonary resuscitation (CPR), continuous monitoring of patients in the emergency room or intensive care unit (ICU), during ambulatory transport, to confirm the correct placement of an endotracheal tube (ETT), and monitoring post-operative patients with a history of sleep apnea or who have received high doses of opioids. Depending on the clinical area, the technology is at various stages of adoption.The growing utility of ETCO2-monitoring technology in diverse clinical settings, the uncertainty regarding the clinical and cost-effectiveness of capnography devices, and access and implementation issues were the main drivers for this health technology assessment (HTA).
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".