Pre-donation Deferral Pattern of Allogeneic Blood Donors: An Analysis from a Developing Country
Bibliographic record
Abstract
Background: Pre-donation donor screening is a crucial step in ensuring the safety of both blood donors and recipients. Donors who do not meet predetermined criteria are temporarily or permanently deferred. Aim: To assess the patterns and prevalence of deferrals at our institution. Study design: Prospective study Place and duration of study: Karachi Tertiary Care Hospital, Karachi from 1st January 2014 to 31st December 2015. Methodology: Thirty six thousand, nine hundred and fifty four potential donors presented themselves, 33853 were selected and 3101 were excluded. Blood donors' demographic information was stored in the blood bank's database, and secondary measures such as the type of deferral (permanent/temporary) and reasons for deferral (donor or patient safety) were evaluated. Results: The majority 2663(7.20%) of donors were deferred due to complete blood count, followed by medical history 264(0.71%) and examination findings 174(0.47%). The majority of donors (96%) were temporarily deferred, while only 3.9% were permanently deferred. Low haemoglobin counts were the most frequent cause of treatment delays (78.8%), followed by hypertension (3.64%) and a history of medication usage (1.32%). Donor safety accounted for the majority of donor rejections (91.5%), while recipient safety accounted for 8.41%. Conclusion: The majority of donors were deferred due to abnormality in the profile of blood count mainly low hemoglobin level. The low hemoglobin counts were the most frequent cause of treatment delays, followed by hypertension and a history of medication usage. Only small numbers of donors were permanently deferred. Keywords: Blood donor, Deferral, Permanent, Temporary
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".