Is Bedside End-Tidal CO<sub>2</sub> Measurement a Screening Tool to Exclude Pulmonary Embolism in Emergency Department?
Bibliographic record
Abstract
Background: Pulmonary embolism (PE) is among the most difficult conditions to diagnose in emergency department. The majority of patients thought to have PE are tested positive for D-dimer and subsequently tested with advanced diagnostic modalities. Novel noninvasive tests capable of excluding PE may obviate the need for advanced imaging tests. We studied the role of combined clinical probability assessment and end-tidal carbon dioxide (ETCO 2 ) measurement for diagnosis of possible PE in emergency department. Methods: We included 100 consecutive subjects suspected to have PE and a positive D-dimer test to study clinical probability of PE and ETCO 2 levels. ETCO 2 > 34 mm Hg was found to be the best cut-off point for diagnosing PE. PE was ultimately eliminated or diagnosed by spiral computed tomography (CT). Results: Diagnostic performances of tests were as follows: ETCO 2 and D-dimer had a sensitivity of 100% and a negative predictive value (NPV) of 100% at the cut-off levels of 34 mm Hg and 500 ng/mL, respectively; Wells score had a sensitivity of 80% and NPV of 69.7% at a score of 4. Conclusions: ETCO 2 alone cannot reliably exclude PE. Combining it with clinical probability, however, reliably and correctly eliminates or diagnoses PE and prevents further testing to be done. J Clin Med Res. 2019;11(10):696-702 doi: https://doi.org/10.14740/jocmr3941
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".