Multi‐center evaluation of the highly sensitive Abbott <scp>ARCHITECT</scp> and Alinity thyroglobulin chemiluminescent microparticle immunoassay
Bibliographic record
Abstract
BACKGROUND: Thyroglobulin (Tg) is an essential part for the management of patients with differentiated thyroid carcinoma (DTC) after thyroidectomy. Highly sensitive Tg assays are now established in clinical practice as they facilitate follow-up of DTC patients. In this study, we evaluated the recently launched highly sensitive Abbott Tg assay for Alinity and ARCHITECT. METHODS: In this three-center study, Tg values of 447 routine patient samples, characterized for the presence of anti-Tg, were measured with the ARCHITECT Tg assay and compared with the Roche Elecsys TgII assay. In addition, a subset of 154 samples was compared also with the Beckman Tg Access assay and another subset (n = 122) with Abbott Tg on the Alinity i analyzer. RESULTS: LoQ was verified to be less than or equal to 0.1 ng/ml confirming that the Tg assay on ARCHITECT and Alinity is highly sensitive. Correlation of ARCHITECT, Alinity, and Roche was excellent with a slope between 0.9 and 1.1 and a correlation coefficient >0.98. Correlation to Beckmann Tg was also very good, but the differences in absolute values were significant (slope: 1.477). CONCLUSIONS: The Abbott Thyroglobulin assay, which is standardized to CRM-457, demonstrated a high correlation to the Roche and Beckman Tg assays, though good agreement of absolute values was only observed between Abbott and Roche. Strength of correlation and slope were not affected by the presence of anti-Tg indicating that all assays included in the study have a similar susceptibility to anti-Tg.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".