Childhood autoimmune hemolytic anemia: A scoping review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Autoimmune hemolytic anemia (AIHA) is a rare but important cause of morbidity in pediatric hematology patients. Given its rarity, there is little high-quality evidence on which to base the investigation and management of pediatric AIHA. This scoping review aims to summarize the current evidence and highlight key gaps to inform future studies. METHODS: This review searched MEDLINE and the Cochrane CENTRAL Trials Register from 2000 to November 03, 2023. Experimental and observational studies reporting AIHA diagnostic criteria, laboratory workup, or treatment/management in populations with at least 20% of patients ≤18 years were included. RESULTS: Forty-three studies were included, with no randomized controlled trials identified. AIHA diagnostic criteria, diagnostic tests, and treatments were highly variable. First-line treatment approaches include corticosteroids, intravenous immunoglobulin, or both. Approaches to AIHA resistance to first-line therapy were widely variable between studies, but most commonly included rituximab and/or cyclosporine. CONCLUSIONS: We identify a heterogenous group of observational studies into this complex, immune-mediated disorder. Standardized definitions and classifications are needed to guide collaborative efforts needed to study this rare disease. The work done by the CEREVANCE group provides an important paradigm for future 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".