Role of Venous Blood Gase (VBG) Analysis in Patient Triage in the Adult Emergency Department
Bibliographic record
Abstract
Background: In emergency and critical care settings, can a venous blood gas analysis improve clinical decision-making and patient outcomes? Methods: This is a cross-sectional study, conducted between January and June 2022 at a tertiary hospital in Saudi Arabia. Results: A total of 100 patients were included, using a convenience sample technique. Their mean age was 54 years, and theirmain chief complaints were shortness of breath (30%), abdominal pain (15%), and altered level of consciousness (14%). The venous blood gas (VBG) result was abnormal in 86 patients, and predicted the need for early intervention in 69 patients (69.7%). A significant association was found between patients requiring early intervention and those with an abnormal VBG (p=0.0005). Furthermore, the VBG results changed the Canadian Triage and Acuity Scale (CTAS) level in 32 patients (33.68%). A logistic regression analysis revealed that pre-testing factors such as age, gender, comorbidities, and chief complaints were not predictors of VBG results, the need for early intervention, or altered CTAS level. Conclusion: Our study concludes that VBG analysis can play an important role in patient triage in the emergency department (ED), allowing for earlier intervention and potentially improving outcomes.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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 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".