The seasonal distribution of immune thrombotic thrombocytopenic purpura is influenced by geography: Epidemiologic findings from a multi‐center analysis of 719 disease episodes
Bibliographic record
Abstract
Prior studies have suggested that immune thrombotic thrombocytopenic purpura (iTTP) may display seasonal variation; however, methodologic limitations and sample sizes have diminished the ability to perform a rigorous assessment. This 5-year retrospective study assessed the epidemiology of iTTP and determined whether it displays a seasonal pattern. Patients with both initial and relapsed iTTP (defined as a disintegrin and metalloprotease with thrombospondin type motifs 13 activity <10%) from 24 tertiary centers in Australia, Canada, France, Greece, Italy, Spain, and the US were included. Seasons were defined as: Northern Hemisphere-winter (December-February); spring (March-May); summer (June-August); autumn (September-November) and Southern Hemisphere-winter (June-August); spring (September-November); summer (December-February); autumn (March-May). Additional outcomes included the mean temperature in months with and without an iTTP episode at each site. A total of 583 patients experienced 719 iTTP episodes. The observed proportion of iTTP episodes during the winter was significantly greater than expected if equally distributed across seasons (28.5%, 205/719, 25.3%-31.9%; p = .03). Distance from the equator and mean temperature deviation both positively correlated with the proportion of iTTP episodes during winter. Acute iTTP episodes were associated with the winter season and colder temperatures, with a second peak during summer. Occurrence during winter was most pronounced at sites further from the equator and/or with greater annual temperature deviations. Understanding the etiologies underlying seasonal patterns of disease may assist in discovery and development of future preventative therapies and inform models for resource utilization.
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".