How do we approach thrombocytopenia in critically ill patients?
Bibliographic record
Abstract
/l) can be associated with bleeding, even moderate-degree thrombocytopenia is associated with organ failure and adverse prognosis. The aetiology for thrombocytopenia in ITU is often multifactorial and correcting one aetiology may not normalise the low platelet count. The classical view for thrombocytopenia in this setting is consumption associated with thrombin-mediated platelet activation, but other concepts, including platelet adhesion to endothelial cells and leucocytes, platelet aggregation by increased von Willebrand factor release, red cell damage and histone release, and platelet destruction by the complement system, have recently been described. The management of severe thrombocytopenia is platelet transfusion in the presence of active bleeding or invasive procedure, but the risk-benefit of prophylactic platelet transfusions in this setting is uncertain. In this review, the incidence and mechanisms of thrombocytopenia in patients with ITU, its prognostic significance and the impact on organ function is discussed. A practical approach based on the authors' experience is described to guide management of a critically ill patient who develops thrombocytopenia.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".