Rare case of refractory mixed autoimmune hemolytic anemia in a 6-year-old child: a case report
Bibliographic record
Abstract
BACKGROUND: Mixed autoimmune hemolytic anemia (AIHA) shows combined clinical and laboratory characteristics of warm and cold AIHA. It is relatively uncommon in children. Consequently, knowledge about mixed AIHA prevalence, clinical presentation, treatment options, and prognosis in children is limited to very few case reports. CASE PRESENTATION: We describe a six-year-old Asian girl presenting with profound anemia, blood group typing discrepancy and crossmatch incompatibility, post upper respiratory tract infection. Detection of red cell warm and cold reactive autoantibodies, led to the diagnosis of mixed AIHA. Autoantibodies with laboratory evidence of hemolysis persisted despite high dose steroid therapy. Due to the inability to wean further, the patient was subsequently commenced on mycophenolate mofetil to which she seems to be responding. CONCLUSIONS: Mixed AIHA may be notoriously difficult to diagnose and treat. Detailed clinical and laboratory work-up is essential to establish the diagnosis. To the best of our knowledge, this is the first case report of mixed AIHA following upper respiratory tract infection. Awareness of this occurrence is important, as similar to warm AIHA, mixed AIHA should be treated immediately by early initiation of steroid therapy. In addition, prompt supportive care as well as long-term clinical follow-up are required to improve outcomes of these cases.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".