Prevalence of Post Donation Adverse Donor Reactions in a Medical College Hospital at Dhaka
Bibliographic record
Abstract
Objectives: Adequate blood supply that must be safe depends on healthy and also with altruistic volunteers who are inclined to donate blood regardless of the potential risk of discomfort or adverse reactions.Blood donation has an tremendous safety record and most of the donors have a good experience or only a mild symptoms after donation.Although even a very low rate of reactions may pessimistic affect their inclination to donate again.The main aim of our study was to calculate the adverse donor reactions among the blood donors in a tertiary care hospital.Methods: We conducted a prospective study between January to December of 2018 in Department of Transfusion Medicine of Sir Salimullah Medical College Hospital, Dhaka.Knowledgeable medical attendants drew blood from selected donors under guidance of a Medical Officer.All donors were observed during and after the procedure of blood donation for any adverse effect up to 30 minutes.Donors were asked to contact the department if they fill any adverse reaction afterwards.Results: A total of 10056 blood donors were registered to donate blood and of them 9453 (94.004%) were eligible for donation.Among 9453 eligible donors a total of 360 (3.8%) donors experienced adverse reactions.The incidence was 1 in every 27 donations.Majority of donors 101 (28.05%) who experienced adverse effect is of age group of 18-25 years with female predominance 192 (53.33%).Among the 360 donors 151(41.94%)developed vasovagal reactions, 83(23.05%)felt nauseated or vomited, 51(14.1%)hyperventilated, 25 (6.94%) cope with delayed syncope, 22 (6.11%) felt dizziness, 18 (5%) formed a hematoma around site of needle prick and 10 (2.77%) others developed problems with blood flow.No delayed donor reactions were recorded.First time donors have higher frequency 479 (79.43%) of adverse reactions than repeat donors.Conclusion: The prevalence was reasonably low in this study of tertiary center.But still it is a potential problem for the donors, especially the new donors.All donors should be briefed prior to donation about the probable side effects of donation.Donation related adverse reactions are often a multifactorial process and can further be minimized by using previous knowledge to prevent it.Donors with adverse effects must be encouraged for future donations along with donor education.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".