Depressão e hipotireoidismo: Uma revisão sistemática
Bibliographic record
Abstract
Background and Objective: Given the global prevalence of depressive disorders and their potential association with neuroendocrine conditions like hypothyroidism, this study aimed to explore the relationship between hypothyroidism and depression or depressive symptoms. Methods: Using databases such as PubMed, Embase, and CAPES, and following the PRISMA methodology, studies published between 2018 and 2022 were selected. The inclusion criteria encompassed articles in English, Spanish, or Portuguese, using various diagnostic methods, including laboratory tests and clinical interviews or psychological scales. Reviews, animal studies, and other types of non-primary publications were excluded. The quality of the studies was assessed using the Newcastle-Ottawa Scale. Results:Among the 14 selected articles, a significant association between hypothyroidism and depression emerged, particularly in women, including those undergoing hormone replacement therapy. However, data on the relationship between subclinical hypothyroidism and depression were conflicting. Additionally, hypothyroidism as a comorbidity in major depressive disorder may contribute to severe clinical outcomes. Conclusions: These results suggest a possible association between hypothyroidism and depression. This finding underscores the importance of evaluating thyroid function in depressed patients, especially women, for effective diagnosis and treatment, aligned with evidence-based clinical practice.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.018 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".