Difficulties in achieving a sustainable blood supply: report from the first national seminar on blood donation in Lebanon
Bibliographic record
Abstract
BACKGROUND: Lebanon has a decentralized/fragmented transfusion system. The current blood supply does not meet the World Health Organization target of achieving 100% voluntary non-remunerated blood donation (VNRD). There are currently 3 types of donors/donations in Lebanon: replacement/family donations (70-75%), VNRD (20-25%), and compensated donations (5-10%). Remunerated donations are illegal. AIMS: This report summarizes the content of presentations given during the first World Blood Donor Day seminar in Lebanon in June 2017. The aim is to describe the current Lebanese blood supply system and the major road blocks and to suggest practical recommendations that may assist in achieving 100% VNRD. METHODS: The content of presentations given during the first World Blood Donor Day seminar in Lebanon in June 2017 were summarized. RESULTS: The seminar was attended by all major stakeholders involved in transfusion medicine (Lebanese National Committee of Blood Transfusion, Hospital Blood Banks directors, Lebanese Army Blood Bank, Lebanese Red Cross and Donner Sang Compter). CONCLUSIONS: The Ministry of Public Health should focus on performing regular audits regarding the implementation of national guidelines. There is a need for a national blood supply committee, unifying all stakeholders in the transfusion and donation fields. Transfusion medicine should be declared by law as a public health issue and considered a priority for patient safety.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".