Low platelet count as risk factor for infections in patients with primary immune thrombocytopenia: a retrospective evaluation
Bibliographic record
Abstract
Infectious complications are common and sometimes life threatening in patients with immune thrombocytopenia (ITP), mainly due to the immune-suppressive therapy. Recent evidence suggests a potential role of platelets in the inflammation process. In this clinical study, we further investigated the role of thrombocytopenia on infections in patients with primary ITP. We retrospectively evaluated data from the recently published large randomized clinical trial of a cohort of 195 patients with primary ITP, who were randomized for prednisone or high-dose dexamethasone. From 158 patients (81%), data on platelet count and infections within the first month of treatment were collected. In this period, 24% of the ITP patients had an infection. Patients with infection had significant lower platelet counts during the first month of treatment leading to a significant lower therapy response at 1 month and a significant longer hospital stay (14.0 versus 9.8 days). Additionally, Cox regression analysis showed that an increase in platelet count of 20 × 109/L led to a reduction of 52% in infections in the next week, showing low platelet count is a significant risk factor for infection. Platelet transfusion led to an increase in platelet count in ITP patients without infection, but not in patients with infection. In conclusion, infections are common in patients with primary ITP leading to significant worse response rates and a longer hospital stay. Interestingly, low platelet count was independently correlated with an increased risk of infection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".