Risk Markers for Thrombocytopenia in Critically Ill Patients: A Prospective Analysis
Bibliographic record
Abstract
STUDY OBJECTIVE: To identify independent risk markers for thrombocytopenia in critically ill patients. DESIGN: Prospective, observational study. SETTING: Eleven-bed intensive care unit-coronary care unit (ICU-CCU) in a community hospital. PATIENTS: Three hundred sixty-two consecutive patients meeting inclusion criteria during 1 year. INTERVENTION: Potential risk marker data were collected on admission to the ICU-CCU and for the period before development of thrombocytopenia (defined as two or more consecutive platelet counts < 150 x 10(3)/mm3 obtained at least 12 hours apart), or for the duration of ICU-CCU stay if thrombocytopenia did not develop. MEASUREMENTS AND MAIN RESULTS: Thrombocytopenia developed in 68 patients (18.8%). Multivariate logistic regression analyses identified patients at risk on admission, but the predictive, potential of the regression model improved when all risk marker exposures during the ICU-CCU stay were considered. Independent risk markers included fresh frozen plasma administration, sepsis, musculoskeletal diagnosis, pulmonary artery catheter insertion, gastrointestinal diagnosis, packed red blood cell administration, and nonsurgical respiratory diagnosis. Higher admission platelet count and aspirin administration were associated with a lower risk of thrombocytopenia. Heparin administration was not identified as a risk marker, and no patient developed heparin-induced thrombocytopenia with thrombosis. Patients with thrombocytopenia had longer ICU-CCU and hospital stays, and higher ICU-CCU and hospital mortality than those without thrombocytopenia. CONCLUSIONS: Development of thrombocytopenia in critically ill patients is associated with specific diagnoses, packed red cell and fresh frozen plasma transfusions, pulmonary artery catheter insertion, and admission platelet count.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".