The Onerous task of managing paroxysmal nocturnal hemoglobinuria in a Low resource setting: a case report. A hematologist’s experience
Bibliographic record
Abstract
Introduction: Paroxysmal nocturnal hemoglobinuria (PNH) is a form of red cell membrane defect characterized by increased sensitivity to complement-mediated cell lysis, resulting in intravascular hemolytic anemia, passage of hemoglobin-containing urine, a high risk of venous thrombosis and progression to pancytopenia.The diagnosis of PNH is based on the flow cytometric (FCM) detection of peripheral blood cell clones. Such clones lack expression of the surface molecules linked to the glycosylphosphatidylinositol (GPI) anchors. The underlying defect is a somatic mutation of the short arm of the phosphatidylinositol glycan class A gene (PIG-A). Case report: We report a case of a 34-year-old male, with recurrent hemolytic anemia and dural venous sinus thrombosis found to have PNH by flow cytometry. He is currently on anticoagulation, iron and folate supplements, intermittent steroids for hemolytic episodes as we await complement inhibitors. Conclusion: As part of the diagnostic workup for patients presenting with recurrent hemolytic anemia and thrombosis in unusual sites, clinicians should include PNH on the list of differential diagnoses. Effort should be taken to characterize the red urine reported on the urine dipstick as blood/hemoglobin by microscopy to differentiate hematuria and hemoglobinuria and order for flow cytometry as this has implications on patient management.
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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) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".