A higher cut-off for Thyroid-stimulating immunoglobulin (TSI) could better predict relapse in Graves’ disease?
Bibliographic record
Abstract
Background: TSH receptor (TSHr)-stimulating immunoglobulins (Igs) can be used as diagnostic markers of Graves’ disease (GD). Thyroid-stimulating immunoglobulin (TSI) assays exclusively detect these specific Igs. Materials and Methods: This was a prospective longitudinal study in which hyperthyroid patients with GD and toxic nodular goitres were evaluated at diagnosis. GD patients were also evaluated at antithyroid drug (ATD) withdrawal. An automated chemiluminescent assay measured TSI. According to the manufacturer TSI less than 0.55 IU/L was a non-reactive result. The authors evaluated the Se and Sp of the cutoff point provided by the TSI assay manufacturer, and tested other cutting points through a ROC curve, to assess relapse risk of Graves’ disease. Results: At diagnosis, were evaluated 92 (85.2%) GD patients aged 41.2 ± 2.0 years, and 16 patients (14.8%) with toxic multinodular goiter (TMNG) or toxic adenoma (TA), aged 60.8 ± 4.8 years. They were re-evaluated after 18 ± 4 months with methimazole (MMI) treatment. The follow-up after treatment suspension was of 20 ± 6 months. At diagnosis, the TSI (Se) and (Sp) were 98.9% and 100%, respectively. At ATD withdrawal, despite a high Se (95.5%), Sp was low (59.6%). By adjusting the cut-off to 1.11 (TSI <1.11 IU/L non-reactive), TSI presented the best Sp (89.4%) with a small decrease in Se (93.3%) in predicting GD relapse. Conclusions: TSI had high Se and Sp in GD differential diagnosis with nodular goiters. In the assessment for GD relapse, by raising the cutting point to 1.11 IU/L, a better Sp was obtained at the expense of a small drop in Se. A larger sample is needed to support a higher TSI cut-off point in the clinical routine to assess GD relapse after ATD.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".