Paroxysmal nocturnal haemoglobinuria, diagnosis and haematological findings, first report from Iran, model for developing countries
Bibliographic record
Abstract
Since paroxysmal nocturnal haemoglobinuria (PNH) was first described in 1881, the diagnosis and follow-up patients diagnosed with the illness has remained an area of concern, with several different techniques of varying sensitivity having been described in the literature for both the diagnosis and monitoring treatment of the disease. PNH is a rare and life-threatening disease that manifests symptoms of haemolytic anaemia. Hence, a quick and reliable technique for precise diagnosis would be crucial. PNH patients who have previously been diagnosed with aplastic anaemia or myelodysplastic syndrome carry small PNH clones and for more than a century traditional method with low sensitivity was used for such patients. In 2010, the International Clinical Cytometry Society described a highly sensitive method for detection and quantification of different types of PNH clones using multi-colour flow cytometry. In this method, a three-colour flow cytometer is essential to detect PNH affected cells amongst monocytes and granulocytes. This started a new era in the diagnosis of patients who carry small clones of PNH cells. Before this, flow cytometric analysis was used only for detection of PNH cells amongst erythrocytes. By using flow cytometry instruments with more light sources, the sensitivity of detection and quantification of PNH clones would be augmented. However, standardisation and crosstalk compensation would be the most concerning issue. For the first time in Iran, we set up and standardised multi-colour flow cytometry technique to detect PNH cells in erythrocytes and leukocytes at Payvand medical laboratory.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".