404
Bibliographic record
Abstract
Introduction: Blood gas analysis is often used to assess acid-base, ventilation and oxygenation status in critically ill patients. Although arterial blood gas (ABG) analysis remains the gold standard, venous blood gas (VBG) analysis has been shown to correlate with ABG analysis and has been proposed as a safer less invasive alternative to ABG analysis. Hypothesis: The purpose of this study was to evaluate the correlation of VBG analysis plus pulse oximetry (SpO2) with ABG analysis. We hypothesized that when compared to ABG analysis, the combination of VBG analysis plus SpO2 could accurately assess acid-base, ventilation and oxygenation status in undifferentiated critically ill patients. Methods: We performed a prospective cohort study of patients in the emergency department (ED) and intensive care unit (ICU) at a single academic tertiary referral center. Patients were eligible for enrollment if the treating physician ordered an ABG. Statistical analysis of VBG, SpO2 and ABG data was done using paired t-test, Pearson’s chi-square and Pearson’s correlation. Results: There were 156 patients enrolled and 129 patients completed the study. Of the patients completing the study, 53 (41.1%) were in the ED, 41 (31.8%) were in the medical ICU and 35 (27.1%) were in the surgical ICU. The mean difference for pH between VBG and ABG was 0.036 (95% confidence interval 0.029-0.043) with a Pearson’s correlation of 0.938. The mean difference for pCO2 between VBG and ABG was 4.9 mmHg (95% confidence interval 3.7-6.0 mmHg) with a Pearson’s correlation of 0.927. SpO2 correlated well with arterial pO2 as predicted by the standard oxygen-hemoglobin dissociation curve. Conclusions: In this population of undifferentiated critically ill patients, pH and pCO2 on VBG analysis correlated with pH and pCO2 on ABG analysis. SpO2 correlated well with pO2 on ABG analysis. The combination of VBG analysis plus SpO2 provided accurate information on acid-base, ventilation and oxygenation status for undifferentiated critically ill patients in the ED and ICU.
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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 | 0.002 |
| 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) | 0.003 | 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 teacher head, 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".