Autoantibodies to thrombopoietin and the thrombopoietin receptor in patients with immune thrombocytopenia
Bibliographic record
Abstract
Autoantibodies to thrombopoietin (TPO, also termed THPO) or the TPO receptor (cMpl, also termed MPL) could play a pathological role in immune thrombocytopenia (ITP). In this study, we tested for autoantibodies against TPO, cMpl, or the TPO/cMpl complex in ITP and other thrombocytopenic disorders. Using an inhibition step with excess TPO in fluid-phase to improve binding specificity, the prevalence of anti-TPO autoantibodies was: active ITP: 9/32 (28%); remission ITP: 0/14 (0%); non-immune thrombocytopenias: 1/10 (10%); and healthy controls: 1/11 (9%). Similarly, using an inhibition step with excess cMpl, the prevalence of specific anti-cMpl autoantibodies was: active ITP: 7/32 (22%); remission ITP: 1/14 (7%); non-immune thrombocytopenias: 3/10 (30%); and healthy controls: 0/11 (0%). Two active ITP patients had autoantibodies against the TPO/cMpl complex, but not against TPO or cMpl alone. Anti-TPO or anti-cMpl autoantibodies were found in 44% of ITP patients, and in 40% of patients with other thrombocytopenic disorders. These autoantibodies did not correlate with ITP disease severity or number of ITP treatments received; however, in this cohort, 3 patients failed to respond to TPO receptor agonist medications, and of those, 2 had anti-TPO autoantibodies. This suggests that anti-TPO and anti-cMpl autoantibodies are associated with thrombocytopenia, and may be clinically relevant in a subset 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".