Identification of Performance Problems in a Commercial Human Immunodeficiency Virus Type 1 Enzyme Immunoassay by Multiuser External Quality Control Monitoring and Real-Time Data Analysis
Bibliographic record
Abstract
In June 2005, a pilot program was implemented in Canadian laboratories to monitor the performance of the Abbott human immunodeficiency virus types 1 and 2 (HIV-1/2) gO enzyme immunoassay (EIA). Two different external quality control (QC) reagents and a "real-time" software analysis program were evaluated. In November 2005, higher-than-expected calibrator rate values in these kits were first reported at the Ontario Ministry of Health (Etobicoke), followed by the Alberta Provincial Public Health Laboratory (Edmonton and Calgary) and others. These aberrations were easily and readily tracked in "real time" using the external QC reagents and the software program. These high calibrator values were confirmed in Delkenheim, Germany, by Abbott, and a manufacturing change was initiated beginning with lot 38299LU00, which was distributed to laboratories in Canada in April 2006. However, widespread reports of calibrator failure by laboratories outside Canada were made in March 2006. In April 2006, Abbott Diagnostics initiated a level III investigation to identify the root cause, which was prolonged storage, under uncontrolled storage conditions, of the raw material used in the manufacture of the matrix cells. To the best of our knowledge, this is the first example of a program in Canada for serological testing that combines a common external QC reagent and a "real-time" software program to allow laboratories to monitor kit performance. In this case, external QC monitoring helped identify and confirm performance problems in the Abbott HIV-1/2 gO EIA kit, further highlighting the benefit of implementing such a program in a national or multilaboratory setting for laboratories performing diagnostic and clinical monitoring testing.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".