Standardization of bleeding assessment in immune thrombocytopenia: report from the International Working Group
Bibliographic record
Abstract
In a previous publication on new terminology, definitions, and outcome criteria for immune thrombocytopenia (ITP), the International Working Group (IWG) on ITP acknowledged that response to treatment should consist of clinically meaningful end points such as bleeding manifestations and that platelet count may not be the ideal parameter for capturing the benefits of therapy. The IWG now proposes a consensus-based ITP-specific bleeding assessment tool (ITP-BAT) with definitions and terminology consistent with those adopted for other bleeding disorders. Bleeding manifestations were grouped into three major domains: skin (S), visible mucosae (M), and organs (O), with gradation of severity (SMOG). Each bleeding manifestation is assessed at the time of examination. Severity is graded from 0 to 3 or 4, with grade 5 for any fatal bleeding. Bleeding reported by the patient without medical documentation is graded 1. Within each domain, the same grade is assigned to bleeding manifestations of similar clinical impact. The "worst bleeding manifestation since the last visit" (observation period) is graded (a suitable database collection form is provided), and the highest grade within each domain is recorded. The SMOG system provides a consistent description of the bleeding phenotype in ITP, and the IWG unanimously supports its adoption and validation in future clinical studies.
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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 | 0.153 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".