Thrombocytopenia in the critically ill: prevalence, incidence, risk factors, and clinical outcomes La thrombocytopenie chez les personnes gravement malades: prevalence, incidence, facteurs de risque et pronostics cliniques
Bibliographic record
Abstract
Purpose The aim of this cohort study was to describe the prevalence, incidence, and risk factors for thrombocytopenia in the intensive care unit (ICU) and to evaluate the impact of thrombocytopenia on mortality with further comparisons amongst major diagnostic categories. Methods Patients admitted to the ICU from 1997-2011 for cardiac, medical, surgical, and trauma conditions were included. The presence of a platelet 100 9 10 9 L -1 on admission day or its appearance during ICU stay were considered as prevalent and incident thrombocytopenia, respectively. Risk factors for thrombocytopenia and the influence of thrombocytopenia on mortality were also analyzed. Results This study included 20,696 patients. Prevalent and incident thrombocytopenia occurred in 13.3% and 7.8% of patients, respectively, with associated mortality rates of 14.3% and 24.7%, respectively, compared with 10.2% in the group with normal platelet count (P 0.001). After adjustments, thrombocytopenia remained associated with an increased risk of mortality (odds ratio 1.25; 95% confidence interval 1.20 to 1.31; P 0.001). The greatest impact of thrombocytopenia on mortality was observed in the cancer, respiratory, digestive, genitourinary, and infectious diagnostic categories. Independent risk factors included age, female sex, admission platelet counts and hemoglobin, mechanical ventilation, days of hospitalization prior to ICU admission, liver cirrhosis, hypersplenism, coronary bypass grafting, intra-aortic balloon pump placement, acute hepatitis, septic shock, and pulmonary embolism or deep vein thrombosis. Conclusions Thrombocytopenia in the ICU is associated with an independent risk of mortality that varies greatly depending on diagnostic admission category.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".