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Record W2917546023 · doi:10.21141/pjp.2019.17

External Quality Assessment Scheme for Transfusion Transmissible Infections Among Blood Service Facilities in the Philippines, 2018

2019· article· en· W2917546023 on OpenAlexaboutno aff
Kenneth Aristotle Punzalan, Rhoda Yu, Iza Mae Chamen

Bibliographic record

VenuePhilippine Journal of Pathology · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBlood donation and transfusion practices
Canadian institutionsnot available
Fundersnot available
KeywordsExternal quality assessmentConcordanceMedicineTest (biology)Computer scienceInternal medicinePathology

Abstract

fetched live from OpenAlex

External Quality Assessment Scheme (EQAS) is an important and vital component of a quality system to which a retrospective and periodic assessment of quality can be undertaken by an independent external agency.The Transfusion Transmissible Infections–National Reference Laboratory (TTI-NRL) annually provides an EQAS program for transfusion transmissible infections to all blood service facilities in the Philippines as a requirement for the renewal of their license to operate and raise the quality standards of testing for infectious diseases.A total of 188 participants registered in the 2018 test event and were given an EQAS panel comprised of a serology program (HVHT4120) and malaria program (MLRA415). Results from the participants 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 from the TTI-NRL.The HVHT4120 program generated 15,330 results and the MLRA415 generated 940 results. 97 results (0.63%) and 80 results (8.51%) were reported as aberrant from each program respectively and were either due to random or systematic errors.The data generated from this test event are used for the improvement of the quality processes of each participant and the subsequent renewal of their license to operate as required by local health regulations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.217
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.043
GPT teacher head0.306
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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