Comparison of the Effectiveness of Hydroxyethyl Starch (Voluven) Solution With Normal Saline in Hemorrhagic Shock Treatment in Trauma
Bibliographic record
Abstract
BACKGROUND: Appropriate fluid therapy affects morbidity and mortality rates. A conclusion is yet to be reached on the role of crystalloids and colloids in immediate fluid therapy. This study was done to determine the suitable solution in immediate resuscitation of patients with hemorrhagic shock caused by tissue trauma. METHODS: One hundred trauma patients with hemorrhagic shock, who underwent fluid therapy in the emergency unit, were assigned randomly to two groups of hydroxyethyl starch (Voluven) and normal saline. Before and after fluid therapy, 1 cc of blood was taken from all patients in order to determine and compare base excess levels. RESULTS: In hydroxyethyl starch (Voluven) and normal saline groups, base excess level after solution therapy increased about 9.65 and 5.46 volumes, respectively, in which augmentation in hydroxyethyl starch (Voluven) group is significantly higher than normal saline group (P ≤ 0.001). CONCLUSION: By using hydroxyethyl starch (Voluven) for fluid therapy in hemorrhagic shock caused by trauma, serum base excess decreases and results in improvement in tissue perfusion and better balance in acid-base status and it seems to be superior over normal saline administration, but the building block of the ideal fluid therapy should still remain with the physician's final clinical judgment.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".