Analysis of Reasons of Blood Donor Deferral at a Tertiary Care Institute in India and Its Reflections on Community Health Status
Bibliographic record
Abstract
INTRODUCTION: Safe blood donors form the backbone of safe blood transfusion services. Donor eligibility policies are a critical layer of blood safety designed to ensure selection of healthy donors and to protect recipients from any harm. This study was planned to analyze the pattern of whole blood donor deferrals, its characteristics and reasons at a tertiary care institute in Northern India, as the pattern varies according to epidemiology of diseases in different demographic areas. MATERIAL & METHODS: It was a cross sectional study of 2 years duration from December 2015 to November 2017. The data of the potential donors who were deferred was recorded on a separate proforma which included their demographic details, type of donation- voluntary donor (VD) and replacement donor (RD); first time (FT) and repeat donor (RPT); type of deferrals (permanent and temporary) and the reasons of deferrals. RESULTS: Three thousand one hundred and thirty-three donors (voluntary-1446 and replacement-1687) donated and 597 donors were deferred (deferral rate- 16%) during this period. Majority of the deferrals i.e. 525 (88%) were temporary, 72 (12%) were permanent. The most common reason of temporary deferral was anemia. The commonest reason of permanent deferrals was a medical history of jaundice. CONCLUSION: Our study results indicate that the blood donor deferral can have subtle variations based on regional aspects that should be considered when national policies are developed as pattern of deferral varies according to epidemiology of diseases in different demographic areas.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".