Human Albumin Use in Adults in U.S. Academic Medical Centers
Bibliographic record
Abstract
OBJECTIVE: To determine rates and predictors of albumin administration, and estimated costs in hospitalized adults in the United States. DESIGN: Cohort study of adult patients from the University HealthSystem Consortium database from 2009 to 2013. SETTING: One hundred twenty academic medical centers and 299 affiliated hospitals. PATIENTS: A total of 12,366,264 hospitalization records. INTERVENTIONS: Analysis of rates and predictors of albumin administration, and estimated costs. MEASUREMENTS AND MAIN RESULTS: Overall the proportion of admissions during which albumin was administered increased from 6.2% in 2009 to 7.5% in 2013; absolute difference 1.3% (95% CI, 1.30-1.40%; p < 0.0001). The increase was greater in surgical patients from 11.7% in 2009 to 15.1% in 2013; absolute difference 3.4% (95% CI, 3.26-3.46%; p < 0.0001). Albumin use varied geographically being lowest with no increase in hospitals in the North Eastern United States (4.9% in 2009 and 5.3% in 2013) and was more common in bigger (> 750 beds; 5.2% in 2009 and 7.3% in 2013) compared to smaller hospitals (< 250 beds; 4.4% in 2009 to 6.2% in 2013). Factors independently associated with albumin use were appropriate indication for albumin use (odds ratio, 65.220; 95% CI, 62.459-68.103); surgical admission (odds ratio, 7.942; 95% CI, 7.889-7.995); and high severity of illness (odds ratio, 8.933; 95% CI, 8.825-9.042). Total estimated albumin cost significantly increased from $325 million in 2009 to $468 million in 2013; (absolute increase of $233 million), p value less than 0.0001. CONCLUSIONS: The proportion of hospitalized adults in the United States receiving albumin has increased, with marked, and currently unexplained, geographic variability and variability by hospital size.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".