Clinical Challenges of Transitioning to High Sensitivity Thyroglobulin Assay
Bibliographic record
Abstract
Background: Patients treated for Differentiated Thyroid Cancer (DTC) are followed by thyroglobulin (Tg) testing with standard Tg assays after a stimulation test (Tg-ST), which is expensive and time consuming. High-Sensitivity Thyroglobulin (HS-Tg) assays are purported to replace Tg-ST; acceptable cutoffs however may vary according to assays and patient population. We aimed to evaluate the HS-Tg assay (Roche Elecsys® Tg II) and apply it to clinical use. Method: Analytical evaluations were performed following CLSI standard protocols. Clinical evaluation was done prospectively on 35 DTC patients subjected to Tg measurements both pre- and post- Tg-ST Clinical accuracy performance of HS-Tg was compared to that of conventional Tg-ST protocol utilizing the Siemens Immulite 2000 XPI platform. Results: HS-Tg assay showed an excellent precision (CV=2-3%). The assay reached CV of 11.0% in pooled samples at a mean of 0.048μg/L. HS-Tg results are slightly higher than those from the conventional Siemens Tg assay. HS-Tg ≥ 0.2μg/L showed clinical sensitivity of 1.0 and specificity of 0.81 for predicting recurrence of DTC, which is superior to the Tg-ST protocol using 2μg/L as the cutoff. Two of six patients with HS-Tg results between 0.06-0.2 had a Tg-ST result of >2μg/L, but no recurrence. Conclusions: Although the HS-Tg cut-off of 0.2ug/L is a reliable alternative to Tg-ST in most cases, these tests show divergent results in a proportion of patients making transition from one test to another challenging. Further studies are required to determine the clinical significance of HS-Tg between 0.06-0.2 μg/L.
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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 | 0.038 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".