Platelet autoantibodies in the bone marrow of patients with immune thrombocytopenia
Bibliographic record
Abstract
Autoantibodies cause platelet destruction in patients with immune thrombocytopenia (ITP); yet only 50% to 60% of patients have detectable platelet autoantibodies in peripheral blood. We hypothesized that in some ITP patients, platelet autoantibodies are sequestered in the bone marrow where pathological immune reactions target megakaryocytes or newly formed platelets. In this study, we modified the platelet glycoprotein-specific assay to test bone marrow aspiration samples for free platelet autoantibodies or antibodies bound to bone marrow cells in aspirate fluid from patients with ITP (n = 18), patients with nonimmune thrombocytopenia (n = 3), and healthy donors (n = 6). We found that 10 (56%) of 18 patients with ITP had autoantibodies in the bone marrow, including 5 (50%) of 10 with autoantibodies in bone marrow only, and 5 (50%) of 10 with autoantibodies in bone marrow and peripheral blood. In comparison, 6 (33%) of 18 ITP patients had autoantibodies in peripheral blood, most of whom (5 [83%] of 6) also had autoantibodies in bone marrow. Bone marrow autoantibodies were not detected in patients with nonimmune thrombocytopenia or healthy donors; however, peripheral blood autoantibodies were detectable in 1 (33%) of 3 patients with nonimmune thrombocytopenia. The sensitivity of platelet autoantibodies for the diagnosis of ITP increased from 60% (peripheral blood testing) to 72% (peripheral blood and bone marrow testing). Immune reactions limited to the bone marrow may be characteristic of certain subsets of ITP 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".