Induction of Painless Thyroiditis in Patients Receiving Programmed Death 1 Receptor Immunotherapy for Metastatic Malignancies
Bibliographic record
Abstract
CONTEXT: Immunotherapies against immune checkpoints that inhibit T cell activation [cytotoxic T lymphocyte antigen 4 (CTLA-4) and programmed cell death 1 (PD-1)] are emerging and promising treatments for several metastatic malignancies. However, the precise adverse effects of these therapies on thyroid gland function have not been well described. CASE DESCRIPTION: We report on 10 cases of painless thyroiditis syndrome (PTS) from a novel etiology, following immunotherapy with anti-PD-1 monoclonal antibodies (mAb) during treatment for metastatic malignancies. Six patients presented with transient thyrotoxicosis in which thyrotropin binding inhibitory immunoglobulins (TBII) were absent for all, whereas four patients had evidence of positive antithyroid antibodies. All thyrotoxic patients required temporary beta-blocker therapy and had spontaneous resolution of thyrotoxicosis with subsequent hypothyroidism. Four patients presented with hypothyroidism without a detected preceding thyrotoxic phase, occurring 6-8 weeks after initial drug exposure. All of these patients had positive antithyroid antibodies and required thyroid hormone replacement therapy for a minimum of 6 months. CONCLUSIONS: Patients receiving anti-PD-1 mAb therapy should be monitored for signs and symptoms of PTS which may require supportive treatment with beta-blockers or thyroid hormone replacement. The anti-PD-1 mAb is a novel exogenous cause of PTS and provides new insight into the possible perturbations of the immune network that may modulate the development of endogenous PTS, including cases of sporadic and postpartum thyroiditis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".