Incident atrial fibrillation in patients with differentiated thyroid cancer: a meta-analysis
Bibliographic record
Abstract
Differentiated thyroid cancer (DTC) represents the most common form of thyroid neoplasms and is becoming increasingly prevalent. Evidence suggests a possible relationship between DTC diagnosis and subsequent atrial fibrillation (AF). If confirmed, this may present an alarming health risk (AF) in an otherwise condition with a relatively good prognosis (DTC). The aim of this systematic review and meta-analysis is to provide for the first time a pooled estimate of AF incidence in DTC patients in comparison to healthy controls. A detailed search in electronic databases, clinical trial registries and grey literature was performed to identify studies reporting the incidence of AF in DTC patients. Newcastle-Ottawa quality assessment scale was used to assess study quality. We used a random effects (RE) generalized linear mixed model (GLMM) in pooling of individual studies and also calculated a prediction interval for the estimate of a new study. Six observational studies met the eligibility criteria, which included totally 187,754 patients with DTC and 199,770 healthy controls. The median follow-up period was 4.3 to 18.8 years; the incidence rate of AF was 4.86 (95% CI, 3.29 to 7.17, I2 = 96%) cases per 1000 person-years, while the incidence rate ratio was 1.54 (95% CI, 1.44 to 1.65, I2 = 0%, 95% PI, 1.33 to 1.78).This is the first meta-analysis to confirm that patients with DTC are at a high risk for developing AF, which may be attributed to a state of iatrogenic hyperthyroidism due to long-term thyrotropin suppression therapy.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.061 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".