Determinants of long-term outcomes of splenectomy in pediatric autoimmune cytopenias
Bibliographic record
Abstract
Splenectomy is effective in ∼70% to 80% of pediatric chronic immune thrombocytopenia (cITP) cases, and few data exist about it in autoimmune hemolytic anemia (AIHA) and Evans syndrome (ES). Because of the irreversibility of the procedure and the lack of predictions regarding long-term outcomes, the decision to undertake splenectomy is difficult in children. We report here factors associated with splenectomy outcomes from the OBS'CEREVANCE cohort, which prospectively includes French children with autoimmune cytopenia (AIC) since 2004. The primary outcome was failure-free survival (FFS), defined as the time from splenectomy to the initiation of a second-line treatment (other than steroids and intravenous immunoglobulins) or death. We included 161 patients (cITP, n = 120; AIHA, n = 19; ES, n = 22) with a median (minimum-maximum) follow-up of 6.8 years (1.0-33.3) after splenectomy. AIC subtype was not associated with FFS. We found that immunopathological manifestations (IMs) were strongly associated with unfavorable outcomes. Diagnosis of an IM before splenectomy was associated with a lower FFS (hazard ratio [HR], 0.39; 95% confidence interval [CI], 0.21-0.72, P = .003, adjusted for AIC subtype). Diagnosis of an IM at any timepoint during follow-up was associated with an even lower FFS (HR, 0.22; 95% CI, 0.12-0.39; P = 2.8 × 10-7, adjusted for AIC subtype) as well as with higher risk of recurrent or severe bacterial infections and thrombosis. In conclusion, our results support the search for associated IMs when considering a splenectomy to refine the risk-benefit ratio. After the procedure, monitoring IMs helps to identify patients with higher risk of unfavorable outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".