Measurement of expired carbon dioxide, oxygen and volume in conjunction with pretest probability estimation as a method to diagnose and exclude pulmonary venous thromboembolism
Bibliographic record
Abstract
BACKGROUND: The classical alveolar pCO(2)-pO(2) relationship predicts that pulmonary embolism (PE) causes a low ratio of pCO(2)/pO(2) at end expiration. Our purpose was to define a simple protocol to collect expired pCO(2)/pO(2) to diagnose PE. Emergency department patients with suspected PE were enrolled. Clinical pretest probabilities for PE were estimated prior to diagnostic testing using the Canadian score and clinicians' unstructured estimate. Patients provided three 30-s periods of tidal breathing, separated by three deep exhalations. Expired pCO(2), pO(2) and breath volume were measured. All patients underwent standardized objective testing for PE including 90-day follow-up. Diagnosis (PE+) required anticoagulation for image-proven PE within 90 days. RESULTS: Of 200 patients enrolled, 178 were included in final analysis (24 PE+). The mean coefficient of variability for the deep-exhaled and end-tidal pCO(2)/pO(2) ratios were 6.8 +/- 6.7 and 7.5 +/- 6.8%, respectively. Mean pCO(2)/pO(2) ratios were stable throughout the collection periods in both methods. When compared with the deep-exhaled ratio, the end-tidal mean ratio demonstrated slightly better diagnostic utility by the area under the receiver operating characteristic curve. The end-tidal ratios were divided into four interval likelihood ratios and coupled with pretest probability from the two methods to generate three sets of posttest probabilities. Receiver operating characteristic analysis demonstrated good overall diagnostic performance (areas under the curves >0.88) for both posttest probability sets. CONCLUSION: This preliminary work demonstrates that the end-tidal pCO(2)/pO(2) averaged from 30 s of breathing can produce clinically relevant likelihood ratios for the diagnosis and exclusion of PE.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".