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 their main 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, 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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".