Impact of age at diagnosis, sex, and immunopathological manifestations in 886 patients with pediatric chronic immune thrombocytopenia
Bibliographic record
Abstract
Pediatric chronic immune thrombocytopenia (cITP) is a heterogeneous condition in terms of bleeding severity, second-line treatment use, association with clinical and/or biological immunopathological manifestations (IMs), and progression to systemic lupus erythematosus (SLE). No risk factors for these outcomes are known. Specifically, whether age at ITP diagnosis, sex, or IMs impact cITP outcomes is unknown. We report the outcomes of patients with pediatric cITP from the French nationwide prospective cohort OBS'CEREVANCE. We used multivariate analyses to investigate the effect of age at ITP diagnosis, sex, and IMs on cITP outcomes. We included 886 patients with a median (min-max) follow-up duration of 5.3 (1.0-29.3) years. We identified an age cutoff that dichotomized the risk of the outcomes and defined two risk groups: patients with ITP diagnosed <10 years (children) and ≥ 10 years (adolescents). Adolescents had a two to four-fold higher risk of grade ≥3 bleeding, second-line treatment use, clinical and biological IMs, and SLE diagnosis. Moreover, female sex and biological IMs were independently associated with higher risks of biological IMs and SLE diagnosis, second-line treatment use, and SLE diagnosis, respectively. The combination of these three risk factors defined outcome-specific risk groups. Finally, we showed that patients clustered in mild and severe phenotypes, more frequent in children and adolescents, respectively. In conclusion, we identified that age at ITP diagnosis, sex, and biological IMs impacted the long-term outcomes of pediatric cITP. We defined risk groups for each outcome, which will help clinical management and further 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".