Relative efficacy of steroid therapy in immune thrombocytopenia mediated by anti‐platelet GPIIbIIIa versus GPIbα antibodies
Bibliographic record
Abstract
Immune thrombocytopenia (ITP) is characterized by platelet clearance mediated primarily by autoantibodies against the platelet GPIIbIIIa and/or GPIbα. Steroid therapy is a first-line treatment for ITP. However, some patients are refractory to this therapy and currently no method can predict which patients will respond. To evaluate whether steroids are equally efficacious in treating patients with ITP caused by anti-GPIIbIIIa versus anti-GPIbα antibodies, we performed a retrospective study on 176 newly diagnosed patients with acute ITP who had severe bleeding symptoms and were admitted as resident patients to the hospital. The patients were treated first with intravenous administration of high-dose dexamethasone (DXM), followed by oral administration of prednisone. Response to therapy was observed in a majority of patients with antibodies specific for GPIIbIIIa (31/43) or without detectable antibodies against either GPIIbIIIa or GPIbα (36/45). In contrast, the steroid response was significantly lower in patients with anti-GPIbα antibodies (9/34) or with antibodies against both GPIbα and GPIIbIIIa (16/54). The preliminary findings of this study suggest that in future prospective clinical trials including corticosteroids, the anti-GPIbα, and -GPIIbIIIa status should be assessed in order to test its potential relevance in deciding future treatments.
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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) | 0.000 | 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) | 0.001 | 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".