IRON OVER LOAD AND ITS RELATION WITH HAEMOSTATIC PARAMETERS IN BETA-THALASSEMIA PATIENTS
Bibliographic record
Abstract
Background: Patients with beta-thalassemia major have been observed to experience changes in their coagulation profile. These changes include an elongated prothrombin time and partial thromboplastin time as well as bring down levels of natural anticoagulants and coagulation factors. The mechanisms underlying the occurrence of thrombotic tendencies in some thalassemia patients remain unclear. This study aims to examine the alterations in the iron and coagulation profile among beta-thalassemia patients. Methods: After informed consent, 50 children having beta-thalassemia, and 50 healthy controls were included in this study. Blood samples were collected and serum ferritin, haematological and haemostatic parameters were measured. Data was analysed on SPSS-24. Results: The laboratory assessment revealed 43.5% of the patients had thrombocytopenia, 54% had prolonged prothrombin time (PT), and 56% of the patients had prolonged activated partial thromboplastin time (aPTT). All estimated coagulation factors exhibited lower activity levels in comparison to the control group. Serum ferritin exhibited a positive relationship with PT and aPTT and a substantial negative relationship with total platelet count. Conclusion: High serum ferritin levels are associated to abnormal haemostatic parameters in thalassemia patients. Regular monitoring of serum ferritin levels is essential to ensure that thalassemia patients receive appropriate treatment and support. Pak J Physiol 2024;20(3):71–3, DOI: https://doi.org/10.69656/pjp.v20i3.1742
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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 | 0.000 |
| 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".