Accuracy and precision of transcutaneous carbon dioxide monitoring: a systematic review and meta-analysis
Bibliographic record
Abstract
Background Transcutaneous carbon dioxide (TcCO2) monitoring is a non-invasive alternative to arterial blood sampling. The aim of this review was to determine the accuracy and precision of TcCO2 measurements. Methods Medline and EMBASE (2000–2016) were searched for studies that reported on a measurement of PaCO2 that coincided with a measurement of TcCO2. Study selection and quality assessment (using the revised Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2)) were performed independently. The Grading Quality of Evidence and Strength of Recommendation approach was used to summarise the strength of the body of evidence. Pooled estimates of the mean bias between TcCO2 and PaCO2 and limits of agreement with outer 95% CIs (termed population limits of agreement) were calculated. Results The mean bias was −0.1 mm Hg and the population limits of agreement were −15 to 15 mm Hg for 7021 paired measurements taken from 2817 participants in 73 studies, which was outside of the clinically acceptable range (7.5 mm Hg). The lowest PaCO2 reported in the studies was 18 mm Hg and the highest was 103 mm Hg. The major sources of inconsistency were sensor location and temperature. The population limits of agreement were within the clinically acceptable range across 3974 paired measurements from 1786 participants in 44 studies that applied the sensor to the earlobe using the TOSCA and Sentec devices (−6 to 6 mm Hg). Conclusion There are substantial differences between TcCO2 and PaCO2 depending on the context in which this technology is used. TcCO2 sensors should preferentially be applied to the earlobe and users should consider setting the temperature of the sensor higher than 42°C when monitoring at other sites. Systematic review registration number PROSPERO; CRD42017057450.
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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 | 0.049 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.039 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".