Utilization of platelet transfusions in the intensive care unit: indications, transfusion triggers, and platelet count responses
Bibliographic record
Abstract
BACKGROUND: A description of current platelet (PLT) transfusion practice in the intensive care unit (ICU) is needed. STUDY DESIGN AND METHODS: All thrombocytopenic patients (PLT count, <150 x 10(9)/L) who received PLT transfusions were identified from a previous prospective study of consecutive medical-surgical ICU patients; trauma, orthopedic, and cardiac surgery were exclusions. Risk factors for ineffective transfusions were examined. RESULTS: Of 261 ICU patients, 118 (45.2%) had thrombocytopenia and a PLT count nadir of less than 50 x 10(9) per L (n = 22), 50 to 99 x 10(9) per L (n = 37), and 100 to 149 x 10(9) per L (n = 59). Twenty-seven (22.9%) patients received PLT transfusions (n = 76 transfusions) and 37 (31.4%) had major bleeding. PLT dose was approximately 3 to 4 x 10(11) per L transfusion. Therapeutic (n = 24) and prophylactic (n = 52) PLT transfusion triggers were 51 x 10(9) per L (interquartile range [IQR], 26 to 68) and 41 x 10(9) per L (IQR, 20 to 57), respectively, as measured at a median of 4.5 hours (IQR, <1.6 to 6.9) before transfusion. A single PLT transfusion resulted in a median PLT increase of 14 x 10(9) per L (IQR, -2 to 30) measured at 5.2 hours (IQR, 1.8 to 8.8) after the transfusion; however, no PLT count increase was observed after 17 transfusions given to 13 (48.1%) patients. No risk factors for ineffective transfusions were identified. CONCLUSIONS: Among critically ill patients, most PLT transfusions were administered to prevent, rather than to treat, bleeding, with a transfusion trigger of 40 to 50 x 10(9) per L. Nearly half of ICU patients who received transfusions failed to mount a PLT count increase after a single transfusion. Prospective studies are needed to determine the effects of PLT transfusions on bleeding and predictors of ineffective transfusions in the ICU.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".