Red blood cell transfusion practices in acute lung injury: What do patient factors contribute?*
Bibliographic record
Abstract
OBJECTIVE: To describe red blood cell (RBC) transfusion practices and evaluate the association between patient-related factors and pretransfusion hemoglobin concentration in acute lung injury (ALI). DESIGN: Secondary analysis of prospectively collected data. SETTING: Nine intensive care units (ICUs) in three teaching hospitals in Baltimore, MD. PATIENTS: Two hundred forty-nine consecutive patients with ALI requiring mechanical ventilation. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Simple and multiple linear regression analyses were used to evaluate the association between the nadir hemoglobin concentration on the day of initial RBC transfusion and 20 patient-level demographic, clinical and ICU treatment factors as well as ICU type. Of 249 patients with ALI, 47% received an RBC transfusion in the ICU without evidence of active hemorrhage or acute cardiac ischemia. The mean (sd) nadir hemoglobin on the day of first transfusion was 7.7 (1.1) g/dL with 67%, 36%, 15%, and 5% of patients transfused at >7, >8, >9, and >10 g/dL, respectively. Transfused patients received a mean (sd) of 5 (6) RBC units from ALI diagnosis to ICU discharge. Prehospital use of iron or erythropoietin and platelet transfusion in the ICU were independently associated with lower pretransfusion hemoglobin concentrations. No patient factors were associated with higher hemoglobin concentrations. Admission to a surgical (vs. medical) ICU was independently associated with a 0.6 g/dL (95% confidence interval 0.1-1.1 g/dL) higher pretransfusion hemoglobin. CONCLUSIONS: Patients with ALI commonly receive RBC transfusions in the ICU. The pretransfusion hemoglobin observed in our study was lower than earlier studies, but a restrictive strategy was not universally adopted. Patient factors do not explain the gap between clinical trial evidence and routine transfusion practices. Future studies should further explore ICU- and physician-related factors as a source of variability in transfusion practice.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".