Primary versus Secondary Immune Thrombocytopenia (ITP): A Meeting Report from the 2023 McMaster ITP Summit
Bibliographic record
Abstract
Abstract The McMaster Immune Thrombocytopenia (ITP) Summit, held on October 27, 2023, was an educational seminar from leading experts in immune thrombocytopenia and related disorders geared toward hematologists, internists, immunologists, and clinical and translational scientists. The focus of the Summit was to review the mechanisms, diagnosis, and treatment of primary versus secondary ITP. Specific objectives were to describe the unique features of secondary ITP, and to review its mechanisms in the context of autoimmune disease and infection. The key messages in this Summit were: (1) ITP is a heterogeneous disease, and genetic and immunologic insights may help classify patient subtypes; (2) exploring the autoimmune mechanisms and their association with hypogammaglobulinemia in patients with secondary ITP could improve our understanding of ITP and its subtypes; (3) investigating the mechanisms of ITP in the context of infections caused by viruses such as CMV, HIV, dengue, and hepatitis C, or bacteria such as H. pylori, or vaccinations could provide insight into the causes of ITP. A better understanding of secondary ITP could help elucidate the pathogenesis of ITP.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".