Neutrophil and Monocyte Counts in Heparin-Induced Thrombocytopenia
Bibliographic record
Abstract
BACKGROUND: Heparin-induced thrombocytopenia (HIT) antibodies activate platelets, monocytes and neutrophils. Despite these findings, it is unknown whether white blood cell (WBC) counts, including neutrophils and monocytes, are altered during HIT. MATERIALS AND METHODS: We evaluated changes in total WBC counts (including WBC subsets), in 50 post-cardiac surgery patients with serologically confirmed HIT (30 patients with HIT-associated thrombosis). Daily leukocyte counts were compared with those measured one day prior to HIT onset; WBC increases were classified as mild (20.0-49.9%), moderate (50.0-99.9%) or major (≥ 100% increase). We also compared changes in WBC counts in HIT patients with and without HIT-associated thrombosis, and non-HIT patients with thrombosis. RESULTS: Most (34/50 [68.0%]) patients with HIT developed WBC count increases (mild, 35.3%; moderate, 44.1%; major, 20.6%). The peak WBC count occurred on day 4 (median) of HIT, which corresponded to day 10 (median) post-surgery. Absolute neutrophil counts increased in most patients (38/50 [76.0%]); whereas absolute monocyte counts rose in some patients, the overall tendency was for the monocyte count to decrease during HIT. Unexpectedly, we found that the increase in total WBC counts, as well as in neutrophils, was seen mainly in patients who developed HIT-associated or non-HIT-associated thrombosis; in contrast, no difference in monocyte levels was seen in patients with or without thrombosis. CONCLUSION: Leukocytosis and neutrophilia are commonly observed in patients with HIT, particularly in patients with HIT-associated thrombosis, as well as non-HIT patients with thrombosis. Thus, leukocytosis/neutrophilia should not infer automatically a diagnosis of infection or inflammation, when evaluating thrombocytopenia in heparin-exposed patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".