Proceedings of the immune thrombocytopenia summit: new concepts in mechanisms, diagnosis, and management
Bibliographic record
Abstract
The inaugural McMaster Immune Thrombocytopenia (ITP) Summit was held virually in 2021. The objectives of the Summit were to recognize the difficulties in establishing the diagnosis of ITP and to understand gaps in current knowledge of ITP mechanisms that might lead to better diagnostic approaches and treatments. The half-day program consisted of virtual educational sessions targeting clinicians and basic scientists. The planning committee chose 8 topics to review that would cover current knowledge and inform future research priorities. In this report, we summarized the presentations delivered at the 2021 McMaster ITP Summit and the discussions. Based on the information presented at the Summit, the following research priorities were identified: 1) investigation of platelet production as a target for ITP treatments; 2) characterization of antigen processing and antigen presentation on platelets; 3) interaction between megakaryocytes and the immune system; 4) the role for ITP gene panels; 5) the need for better methods for platelet antibody testing; 6) the role of prediction models for diagnosis and prognosis; 7) new treatment strategies, including intensification of initial therapy; and 8) personalized treatment algorithms.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".