Bibliographic record
Abstract
Despite advance in medical treatment, blood supply is still an essential element in modern healthcare system with blood transfusion used in almost every clinical specialty. Adequacy, quality and safety are always the key focuses of concerns in most countries that measures have been taken to secure them. As in many other healthcare facilities, many countries have on its legislation to license the operation of blood centres (whether they are national, Red Cross or hospitals based) to provide the blood supply for clinical transfusion. The licensing requirement is based on their national health policy and law. Besides, like hospitals and clinical laboratories, many blood centres may also seek to achieve accreditation to demonstrate to their stakeholders of their commitment to ensure quality and safety. Such accreditation may vary from technical requirements related to medical laboratory testing as ISO15189, professional related such as AABB to good manufacturing practice. Irrespective to the type and sources of accreditation, a superior quality management system is often necessary to provide the foundation to meet the accreditation's requirement with ongoing improvement. The study aimed to describe on the preparation of a blood centre towards accreditation. Few key elements will be outlined which include the top management commitment, selection of the right accreditation standard, training of the key personnel and organizational staff, gap analysis, documentation and change in practice if needed, internal and external audits. Tips and advice will also be included to ease the fear of accreditation processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.055 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.018 | 0.008 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.116 | 0.094 |
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".