Thyroid Hormone Autoantibodies: Are They a Better Marker to Detect Early Thyroid Damage in Patients with Hematologic Cancers Receiving Tyrosine Kinase Inhibitor or Immunoregulatory Drug Treatments?
Bibliographic record
Abstract
BACKGROUND: Unlike cytotoxic agents, novel antineoplastic drugs can variably affect thyroid function and so impair patient outcomes. However, the widely used standard thyroid tests have demonstrated low sensitivity for detecting early thyroid damage that leads to dysfunction of the gland. To find a more reliable thyroid marker, we assessed the presence of antibodies binding thyroid hormones (thAbs) in a cancer population undergoing potentially thyrotoxic treatment. METHODS: From April 2010 to September 2013, 82 patients with hematologic malignancies treated with tyrosine kinase inhibitors or immunoregulatory drugs were recruited. Healthy volunteers (n = 104) served as control subjects. Thyroid function, autoimmunity tests, thAbs, and thyroid sonography were assessed once during treatment. RESULTS: Overall, thAb positivity was recorded in 13% of the entire cohort. In most cases, the thAbs were of a single type, with a predominance of T3 immunoglobulin G. More specifically, thAbs were detected in 11 cancer patients; and abnormal levels of thyroid-stimulating hormone, thyroglobulin antibody, and thyroperoxidase antibody were detected in 6 (p = 0.05), 0 (p = 0.0006), and 2 cancer patients (p = 0.001) respectively. Ultrasonographic alterations of the thyroid were observed in 12 cancer patients. In contrast, of the 104 healthy control subjects, only 1 was positive for thAbs (1%). CONCLUSIONS: We have demonstrated for the first time that thAbs are a reliable marker of early thyroid dysfunction when compared with the widely used standard thyroid tests. A confirmatory prospective trial aiming at evaluating thAbs at various time points during treatment could clarify the incidence and timing of antibody appearance.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".