Transfusion Patterns in All Patients Admitted to the Intensive Care Unit and in Those Who Die in Hospital: A Descriptive Analysis
Bibliographic record
Abstract
While it is known that the use of health care resources increases at the end of life in patients admitted to the Intensive Care Unit (ICU), the allocation of blood products at the end of life has not been described. The objective of this study was to describe overall transfusion patterns in the ICU, and specifically in patients who die in hospital. We conducted a retrospective cohort study of adult patients admitted to the ICU of a university-affiliated hospital, who were discharged or died between November 1, 2006 and June 30, 2012. During the study period, 10,642 patients were admitted at least once to the ICU. Of these patients, 4079 (38.3%) received red blood cells (RBCs), plasma or platelets in the ICU. The ICU mortality rate was 28.1% and in-hospital mortality rate was 32.3%. Among 39,591 blood product units transfused over the course of the study in the ICU (18,144 RBC units, 16,920 plasma units and 4527 platelet units), 46.2% were administered to patients who later died within the same hospitalization (41.2% of RBCs, 50.4% of plasma and 50.8% of platelets). Of all blood product units (RBCs, plasma and platelets) administered in the ICU over the study period, 11% were given within the last 24 hours before death. A large proportion of blood products used in the ICU are administered to patients who ultimately succumb to their illness in hospital, and many of these blood units are given in close proximity to death.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".