Effects of anemia and blood transfusion in acute myocardial infarction in rats
Bibliographic record
Abstract
BACKGROUND: The optimal hemoglobin (Hb) level in acute myocardial infarction (MI) is unknown. The goal of this study was to determine the optimal Hb concentration in acute MI and whether transfusion of fresh blood to correct anemia reduces myocardial injury and improves outcome. STUDY DESIGN AND METHODS: Anemia was induced in rats by an iron-deficient diet and phlebotomy. MI was induced by left coronary artery ligation. Some rats received transfusion of fresh blood. Survival, hemodynamic measurements, and infarct size were determined 24 hours after MI. RESULTS: Reduction of Hb to 80 to 90 and 70 to 80 g/L decreased 24-hour survival after MI to 42 and 47%, respectively (p < 0.05). Infarct size was increased in both 70 to 80 and 80 to 90 g/L anemic groups compared to the normal Hb group (p < 0.05). Cardiac function was decreased in anemic groups after MI (p < 0.01). Transfusion of fresh blood to increase Hb from 80 to 90 g/L to 100 g/L decreased infarct size (p < 0.05) and improved cardiac function (p < 0.05), and a trend toward better survival (73%) was observed. Transfusion from 80 to 90 g/L Hb to 120 g/L Hb was associated with larger infarct size (p < 0.05), decreased cardiac function (p < 0.05), and no improvement in survival (47%, p = NS). CONCLUSION: Anemia increases infarct size and decreases cardiac function and survival in acute MI. Transfusion of anemic animals up to 100 g/L Hb with fresh blood reduces infarct size and improves cardiac function. However, transfusion to 120 g/L Hb did not demonstrate any additional benefit and was associated with larger infarcts.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".