External Quality Assessment Scheme for Transfusion Transmissible Infections among Blood Service Facilities in the Philippines, 2015
Bibliographic record
Abstract
The External Quality Assessment Scheme (EQAS) for Blood Screening Serology aims to raise standards and assess the phases of laboratory testing of blood units. In 2015, the National Blood Program listed a total of 200 Blood Service Facilities (BSF)147 of which enrolled for EQAS. These participants were given an EQAS panel designed to check the capacity of a BSF to detect the 5 transfusion transmitted infections (HIV, HBV, HCV, Syphilis and Malaria). Panels should be tested how a blood unit is routinely screened to mimic the actual laboratory process. This allows the NRL and participant to check and validate the entire blood unit screening process. Test results were analyzed by OASYS Canada using the ISO 13528:2005 Robust Statistics method (Huber’s Method) to identify outliers. Data analysis from the test event showed a significant number of participants that reported aberrant results due to errors related to random or systematic errors. This also showed deviations from standard practice recommended by the Department of Health as well as a comparison of different test platforms for blood screening. Ultimately, the data gathered from the EQAS are used to improve on policies for blood screening and set recommendations for the safety of the Philippine blood supply.
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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 | 0.034 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".