Autoimmune Haemolytic Anaemia: A cross sectional study in a Tertiary Haematological Centre
Bibliographic record
Abstract
Autoimmune haemolytic anaemia (AIHA) is a group of disorders wherein autoantibody causes decompensated acquired haemolysis. There has been no epidemiological study of autoimmune haemolytic anaemia (AIHA) in Malaysia. This study retrospectively analysed the epidemiology of AIHA including Evan’s Syndrome in a Tertiary Haematology Centre in Malaysia. Patients diagnosed with AIHA and Evan’s Syndrome at 18 years old and above between 1 January 1994 to 1 October 2020 at the out-patient Haematology Clinic of Hospital Raja Permaisuri Bainun, Ipoh were selected. Patients’ information was retrieved from the outpatient clinic records. A total of 71 patients were included of which predominantly female. The mean age for both genders were comparable. Ethnic stratification revealed AIHA was higher in Malays followed by Chinese and Indian. Warm AIHA was most prevalent at 40.8%, compared to cold AIHA and Evan’s Syndrome (both 23.9%), and mixed AIHA (11.3%). Primary was more common than secondary AIHA followed by Evan’s Syndrome. Approximately half of the secondary AIHA and secondary Evan’s Syndrome were due to SLE. Overall, 67.6% of patients received corticosteroid only and 28.2% combined with immunosuppressant. Individuals at higher age and females have higher risk of developing AIHA and Evan’s Syndrome. The highest prevalence was seen among the Malay ethnic. Primary warm AIHA is the most common type and majority of Evan’s syndrome are secondary to autoimmune diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".