Autoimmune Thyroid Disease and Differentiated Thyroid Carcinoma: A Review of the Mechanisms That Explain an Intriguing and Exciting Relationship
Bibliographic record
Abstract
Autoimmune thyroid disease is a complex and highly frequent disease, where a wide variety of genetic, epigenetic and environmental factors (among others) come together and interact, and is characterized by the presence of two clinical outcomes: hypothyroidism (in Hashimoto's thyroiditis) and hyperthyroidism (in Graves-Basedow disease). For its part, differentiated thyroid carcinoma (mainly papillary carcinoma) is the most common type of cancer affecting the thyroid (and one of the most prevalent worldwide). An important co-occurrence between autoimmune thyroid disease and differentiated thyroid carcinoma has been documented. In this article, studies that have evaluated possible associations and relationships between autoimmune thyroid disease and differentiated thyroid cancer are systematically described and summarized. To date, the underlying mechanism that explains this association is inflammation; however, the characteristics and designs of the studies evaluated do not yet allow a causal relationship between the two entities to be established. These aspects have made it difficult to establish "causality" in the continuum of the pathogenesis between both conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".