Thrombosis with thrombocytopenia syndrome (TTS) and vaccine-induced immune thrombocytopenia and thrombosis (VITT): Brighton Collaboration case definitions and guidelines for data collection, analysis, and presentation of immunisation safety data
Bibliographic record
Abstract
This is a revision of the online November 2021 Brighton thrombosis with thrombocytopenia syndrome (TTS) case definition and a new Brighton Collaboration case definition for vaccine-induced immune thrombocytopenia and thrombosis (VITT). These case definitions are intended for use in clinical trials and post-licensure pharmacovigilance activities to facilitate safety data comparability across multiple settings. They are not intended to guide clinical management. The case definitions were developed by a group of subject matter and Brighton Collaboration process experts as part of the Coalition for Epidemic Preparedness Innovations (CEPI)-funded Safety Platform for Evaluation of vACcines (SPEAC). The case definitions, each with defined levels of diagnostic certainty, are based on relevant published evidence and expert consensus and are accompanied by specific guidelines for TTS and VITT data collection and analysis. The document underwent peer review by a reference group of vaccine safety stakeholders and haematology experts to ensure case definition useability, applicability and scientific integrity.
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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 | 0.076 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".