Thyroid-stimulating hormone and the risk of Alzheimer's disease: an ADNI cohort study
Bibliographic record
Abstract
Abstract Background: The association of thyroid function with Alzheimer's disease (AD) is controversial. This study mainly aimed to investigate the association between thyroid-stimulating hormone (TSH) and the risk of AD. Methods: We investigated the cross-sectional association between TSH and cognition, cerebrospinal fluid (CSF) biomarkers, and neuroimaging by linear regression models. The association between TSH and the risk of MCI conversion to AD within four years was measured by Cox proportional hazards models. Additionally, we investigated the interaction effects between TSH and sex in analyses. Results: A total of 476 participants who measured plasma TSH at baseline were included in the analyses, comprising 49 cognitively normal (CN), 336 mild cognitive impairment (MCI), and 91 AD. Within four years of follow-up, 160 MCI participants converted to AD. No associations were found between TSH and cognition and AD biomarkers. There existed sex differences in the association between TSH within the normal range and the risk of AD (p for interaction = 0.043). The highest tertile of TSH within the normal range significantly increased the risk of AD in female compared to the lowest tertile (HR = 2.62, p = 0.021). TSH was not associated with the risk of AD in male. Conclusions: High plasma levels of TSH within the normal range were associated with an increased risk of AD in female but not in male.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".