Incidence of Thrombotic Thrombocytopenic Purpura/Hemolytic Uremic Syndrome
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it