Incidence of Thrombotic Thrombocytopenic Purpura/Hemolytic Uremic Syndrome
Bibliographic record
Abstract
BACKGROUND: Thrombotic thrombocytopenic purpura and hemolytic uremic syndrome are rare disorders characterized by platelet aggregation, microthrombi, and resulting tissue damage. We studied the incidence and possible risk factors for these diseases in 3 large populations in the United States, United Kingdom, and Canada. METHODS: Data were derived from a large health insurer in the United States, general practices in the United Kingdom, and the Province of Saskatchewan. We identified potential cases of thrombotic thrombocytopenia purpura and hemolytic uremic syndrome in computerized data and verified them by medical record review. We estimated incidence rates for thrombotic thrombocytopenia purpura and hemolytic uremic syndrome together and separately, and we conducted a case-control study to evaluate potential risk factors. RESULTS: The age-sex standardized incidence of thrombotic thrombocytopenia purpura and hemolytic uremic syndrome was higher than previously reported (6.5, 2.2, and 3.2 per million per year in the United States, United Kingdom, and Saskatchewan, respectively), but there was no secular trend. The incidence of thrombotic thrombocytopenia purpura and hemolytic uremic syndrome was higher in women than men. Most cases of hemolytic uremic syndrome occurred before 20 years of age. We confirmed several known risk factors for thrombotic thrombocytopenia purpura and hemolytic uremic syndrome (cancer, bone marrow transplantation, pregnancy). CONCLUSION: The incidence of thrombotic thrombocytopenia purpura and hemolytic uremic syndrome is higher than previously reported but does not appear to be rising. Apparent international differences in incidence could be the result of imprecision in identifying thrombotic thrombocytopenia purpura and hemolytic uremic syndrome in large research databases.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".