International Albumin Use: 1995 to 2006
Bibliographic record
Abstract
Over the last ten years more reliable information regarding the risks and benefits of the use of albumin for fluid resuscitation has emerged. To determine what influence this has had on clinical practice, we sought to document albumin use (from mass of albumin supplied to hospitals) in 16 industrialised countries between 1995 and 2006. Data on national albumin and synthetic colloid use was sought from independent intensive care researchers and albumin issuers. The mass of albumin supplied per 10,000 persons on an annual basis by country and aggregated across the study countries was calculated. Volumes of synthetic colloid supplied per 10,000 persons were calculated. Data were obtained for 15 countries. Albumin use varied significantly between countries and throughout the observation period. Overall, aggregate albumin use decreased from a peak of 2.54 kg per 10,000 persons in 1995 to 1.40 kg per 10,000 persons in 1999; use has remained relatively constant since. Data on supply of synthetic colloids was available in only three countries and varied from 11.7 litres per 10,000 persons in Canada in 1995, to 231.8 litres per 10,000 persons in Denmark in 2004. Between 1995 and 1999 albumin use decreased and has been materially constant since; where data were available, use of synthetic colloids increased. Whether these practice changes have resulted in a net health gain or in harm requires further research.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".