Assessing the Risk of Depression Among U.S. Adults with Resolved Thyroid Dysfunction--data analysis from the National Health and Nutrition Examination Survey (2013–2018)
Bibliographic record
Abstract
Abstract Background Depression is a highly-prevalent disease among US adults. The positive association between thyroid dysfunction and depression has been identified in many studies, but whether the thyroid dysfunction history (have recovered) is still associated with depression, and whether this association is modified by gender is still unknown. This finding can be important for depression preventions. Methods We applied design-adjusted multivariable logistic regression to examine the adjusted association between thyroid dysfunction history and depression by using data from the National Health and Nutrition Examination Survey from 2013 to 2018. Results Of 11975 respondents with complete responses included in this study, 8.4% (n = 1007) of the respondents have depression. The design-adjusted analysis shows no significant association between the thyroid dysfunction history and depression but shows gender as a significant effect modifier of the association. The association is 3.31(odds ratio, 95% CI: [1.38,7.93]) for males and 1.15 (odds ratio, 95% CI: [0.72, 1.84]) for females. Conclusion People who had thyroid dysfunction but recovered will still have a higher risk of getting depression, and it differs in genders. More suggestions and actions are needed for those who recovered from the thyroid dysfunction in order to prevent depression, especially for the males.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".