Treatment of Subclinical Hyperthyroidism and Incident Atrial Fibrillation
Bibliographic record
Abstract
CONTEXT: Treating overt hyperthyroidism prevents atrial fibrillation (AF). Though subclinical hyperthyroidism (SH) has been associated with AF, it is unknown whether treating SH prevents AF. OBJECTIVE: We aimed to identify the association between treating SH and incident AF. DESIGN: In a pharmacoepidemiologic retrospective cohort study, patients diagnosed with SH between 2000 and 2021 were followed. PATIENTS: Outpatients ≥ 18 years with biochemical SH and without prior AF, hypothyroidism, thyroid cancer, pituitary disease, or pregnancy were included. MAIN OUTCOMES: The primary outcome was incident AF. Secondary outcomes were ECG and echocardiographic features associated with AF. RESULTS: Of 2169 patients screened, 360 (131 treated and 229 untreated) were followed up for a mean of 4.27 years. In the treated and untreated groups, AF occurred in 4 (3.1%) and 15 (6.6%) patients (p = 0.15), and AF incidence was 0.8% and 1.4%/year (p = 0.31), respectively. The hazard ratio (HR) for treatment as a time-dependent variable was 0.60 (95% CI 0.19-1.92; p = 0.39). As some cases of AF were documented nearly simultaneously with SH treatment, a sensitivity analysis was performed reassigning two patients diagnosed with AF < 30 days after starting SH treatment to the untreated group. Here, in the treated and untreated groups, AF occurred in 1.6% and 7.4% (p = 0.02), and AF incidence was 0.4% and 1.8%/year (p = 0.02), respectively. The HR was 0.25 (0.06-1.13; p = 0.07). There were no differences in ECG or echocardiographic features. CONCLUSION: There was an overall trend towards lower incidence and prevalence of AF following treatment of SH, supporting the need for larger scale studies.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".