Assessing thrombocytopenia in the intensive care unit: the past, present, and future
Bibliographic record
Abstract
Thrombocytopenia is common among patients admitted to the intensive care unit (ICU). Multiple pathophysiological mechanisms may contribute, including thrombin-mediated platelet activation, dilution, hemophagocytosis, extracellular histones, ADAMTS13 deficiency, and complement activation. From the clinical perspective, the development of thrombocytopenia in the ICU usually indicates serious organ system derangement and physiologic decompensation rather than a primary hematologic disorder. Thrombocytopenia is associated with bleeding, transfusion, and adverse clinical outcomes including death, though few deaths are directly attributable to bleeding. The assessment of thrombocytopenia begins by looking back to the patient's medical history and presenting illness. This past information, combined with careful observation of the platelet trajectory in the context of the patient's clinical course, offers clues to the diagnosis and prognosis. Management is primarily directed at the underlying disorder and transfusion of platelets to prevent or treat clinical bleeding. Optimal platelet transfusion strategies are not defined, and a conservative approach is recommended.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".