Warm Autoimmune Hemolytic Anemia: Clinical Profile and Management
Bibliographic record
Abstract
BACKGROUND: Autoimmune hemolytic anemia (AIHA) is a rare autoimmune disease in which autoantibodies target red blood cells leading to marked decrease in their lifespan. The classification of AIHA is based on the immunochemical properties of the RBC autoantibody. Warm antibody AIHA (wAIHA) accounts for 75-80% of all adult AIHA cases. The treatment of wAIHA is mainly corticosteroids. Our retrospective study aimed to study the clinical profile and management of wAIHA. METHODS: Data of 75 patients admitted with wAIHA or presented to outpatient department (previous medical records) with wAIHA between January 2003 and January 2016 were analyzed. RESULTS: In our study, females constituted 12 and 26 patients of primary and secondary wAIHA, while males constituted 17 and 20 patients of primary and secondary wAIHA, respectively. Mean hemoglobin level at AIHA onset was found to be 7.1 ± 1.7 g/dL in primary wAIHA group and 6.3 ± 1.2 g/dL in secondary wAIHA group, which is statistically significant. Splenectomy was used as mode of treatment in one (3.4%) patient of primary wAIHA group and 15 (32.60%) patients of secondary wAIHA group, which is statistically significant. Mean age of wAIHA onset was 69.7 ± 21.5 years in wAIHA group secondary to lymphoma and 54.3 ± 25.7 years in other wAIHA group, which is statistically significant. CONCLUSION: The most common causes of secondary wAIHA are B-cell lymphoma, systemic lupus erythematosus, rheumatoid arthritis, chronic lymphocytic leukemia (CLL), common variable immune deficiency, renal cell carcinoma and secondary to drug usage (alpha methyldopa and carbamazepine), respectively. Reducing the cumulative dose of corticosteroids with second line treatment whenever possible and therefore reducing the risk of sepsis, specifically in older patients with comorbidities will reduce morbidity and mortality.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".