Successful Treatment of Refractory Autoimmune Hemolytic Anemia (AIHA) in a Child, Based on Iranian Traditional Medicine: A Case Report
Bibliographic record
Abstract
Autoimmune hemolytic anemia (AIHA) is a heterogeneous and relatively unknown disease caused by premature immune destruction of red blood cells.While its occurrence is uncommon among children, it is sometimes severe and resistant to treatment.The warm -reactive type contains 70 % to 80% of all cases, in which the first-line treatment is considered to be a steroid.Moreover, splenectomy, rituximab (a monoclonal antibody), and immunosuppressive drugs are used in refractory cases, with unclear efficacy and deep suppression of the immune system, which consequently lead to various side effects.This study reports the successful treatment of a life-threatening case using a new method.In this regard, it was stated that using the capacity of Iranian traditional medicine (ITM) as one of the complementary therapies can help in the treatment of this disease.In this case report, we documented the successful treatment of a severe and refractory warm AIHA in a boy, who was resistant to the currently recommended treatments such as corticosteroids, rituximab, and cyclosporine at different time periods.Based on ITM, a novel treatment was performed, which was daily swallowing 4-6 live small freshwater fishes (from Cyprinidae family) for an eight-week period and later being tapered.As a result, this treatment had a rapid response with no side effects.At the time of performing this study, the patient was in his 5th-year diseasefree period.For future research, we recommend the researchers to study the use of this novel treatment in case of resistance to the current established therapies of warm AIHA disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".