Atypical autoimmune hemolytic anemia
Bibliographic record
Abstract
9/L (10-90), bilirubin 1.5 mg/dL (0-1.0), lactate dehydrogenase 1715 U/L (normal < 210) and ferritin 697 ng/mL. The blood film showed microcytosis and coarse basophilic stippling consistent with β-thalassemia trait. Only very occasional spherocytes were noted and there was no agglutination. A bone marrow biopsy revealed mild erythroid hyperplasia and otherwise unremarkable morphology. The direct antiglobulin test (DAT) was repeatedly negative for IgG but weakly positive for C3d. Special DAT assays by gel method, cold saline wash and IgM/IgA substrates, were negative. Weak red cell autoantibodies were detectable eventually by polyethylene glycol (PEG) method through a reticulocyte enriched blood specimen, aiming to increase the sensitivity of the DAT assay by reducing the contamination of more aged transfused red cells. Weak red cell autoantibodies with no demonstrable specificity were also detected in the patient’s serum by PEG. An AHA with IgG autoantibody directed against reticulocytes or a mature RBC precursor was suspected based upon the reticulocytopenia and IgG positive DAT restricted to the reticulocyte enriched RBC fraction. 2 Prednisone 1.5 mg/kg/day, i.v. cyclophos phamide (~900 mg/m 2 monthly for five doses) and ritux imab (1,000 mg/m 2 for two doses) were administered with subsequent cessation of transfusion requirements and return to her baseline hemoglobin of 10.5 g/dL. AHA with atypical features, as illustrated above, raises challenges in diagnosis and management. Anemia can be multifactorial and peripheral blood morphology with coexisting conditions such as thalassemia trait 3 may be misleading. Importantly, 5-10% of patients with AHA are “DAT-negative”. A laboratory approach to such cases has been suggested but definitive proof of autoimmune hemolysis is often difficult to establish. 4
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".