Mirtazapine use may increase the risk of hypothyroxinemia in patients affected by major depressive disorder
Bibliographic record
Abstract
Background Hypothyroxinemia, i.e. Low free T4 with normal TSH level, which overlaps, to a great extent, with the laboratory criteria of central hypothyroidism, could be easily neglected, if attention is paid only to patients with elevated TSH. We aimed to assess the association between mirtazapine use and hypothyroxinemia in patients affected by major depressive disorder. Methods We conducted a retrospective cohort study in the Second Affiliated Hospital of Xinxiang Medical University between January 2016 and December 2018. Patients affected by major depression disorder and admitted to the hospital for treatment during the study period and had thyroid tests at admission and after treatment were included. Patients with abnormal thyroid function at baseline or received mood stabilizers or quetiapine during hospitalization were excluded. Mirtazapine use was the exposure measure, and hypothyroxinemia was as the primary outcome of this study. Log-binomial model was used to estimate the association between mirtazapine use and hypothyroxinemia, after adjusting for potential confounding factors. Results A total of 220 eligible patients were included in the final analysis. Of them, 88 used mirtazapine. The incidence of hypothyroxinemia in patients who used mirtazapine was higher (37.5%) than those patients who did not use (19.7%). The relative risk of developing hypothyroxinemia was 1.64 (95% confidence interval: 1.31-1.78) for mirtazapine use, after adjusting for confounding factors. Conclusion Mirtazapine use was associated with the risk of developing hypothyroxinemia. Clinicians should be aware that hypothyroxinemia may be neglected in patients treated by mirtazapine due to attention paid only to those with elevated TSH.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".