Diagnostic accuracy of venous blood gases compared to arterial blood gases
Bibliographic record
Abstract
Background: Arterial blood gases (ABGs) are the gold standard to assess acid-base balance, ventilation and blood oxygenation. However, using venous blood gases (VBGs) could avoid the pain and complications associated to ABGs. No studies have assessed if VBGs are comparable to ABGs in reaching the same diagnosis of normal vs. abnormal pH, CO 2 , and HCO3 - . Objectives: To assess the diagnostic accuracy of VBGs compared to ABGs. Methods: All patients presenting to the emergency department of Kingston General Hospital who required ABGs were eligible for the study. Emergency physicians or residents obtained ABG samples from radial, brachial, or femoral arteries on 218 patients. Nurses immediately drew VBG samples from peripheral veins. Blood gas analyses were performed on the Radiometer ABL 500 and 520 gas analyzers. We calculated the sensitivity and specificity of VBGs, using ABGs as the gold standard. Published normal venous (only one complete set of normal VBGs found) and arterial values were used to determine normal vs abnormal results for pH, CO 2 , and HCO3 - . Results: As shown in table 1, VBGs had low sensitivity and/or specificity for detecting most blood gas abnormalities; this could lead to common misdiagnoses and mismanagement of patients. Accuracy of VBGs compared to ABGs Sensitivity (%) Specificity (%) False Abnormal (%) False Normal (%) pH 77 80 20 23 PCO2 75 64 36 25 HCO3 95 69 31 5 Conclusion: Given the high proportion of false normal and abnormal results using currently available venous blood normal values, VBGs cannot replace ABGs to diagnose acid-base or ventilatory derangements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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; both teacher heads agree on what is shown here.
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".