External Quality Assessment Scheme for Transfusion Transmissible Infections Among Blood Service Facilities in the Philippines, 2017
Bibliographic record
Abstract
The External Quality Assessment Scheme (EQAS) evaluates the performance of participating laboratories through an external agency by which known blinded samples are sent to participants for analysis, and their performance evaluated and monitored. The Transfusion Transmissible Infections – National Reference Laboratory provides an external quality assessment scheme for transfusion transmissible infections to blood service facilities in the Philippines with the aim of raising the standards of quality testing in infectious diseases in blood units and as a mandatory requirement in the licensing of laboratories. In the 2017 test event, 180 participants were given an EQAS panel composed of the HVHT4120 serology program and the MLRA415 malaria program. Results were submitted through an online informatics system managed by OneWorld Accuracy Canada using the ISO 13528:2008 Robust Statistics method (Huber’s Method). Results were analyzed and evaluated with the reference result of the NRL to which non-concordant results would be marked aberrant. From the 14,392 generated results from the HVHT4120 program and 885 generated results from the MLRA415 program, 51 (0.35%) results and 86 (9.72%) results were reported as aberrant respectively. The aberrant results reported were either due to random or systematic errors. Analyzed data from this test event are used for the continuous improvement of their competencies and the renewal of their license to operate as required by the Department of Health.
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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 | 0.041 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| 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) | 0.005 | 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".