Melasma and thyroid disorders: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Thyroid hormones may play a key role in melasma; however, melasma link with thyroid disorders remains controversial. OBJECTIVES: To compare the serum levels of thyroid-stimulating hormone (TSH), T4, T3, anti-thyroid peroxidase (anti-TPO), and antithyroglobulin between patients with melasma and control group using meta-analysis. METHODS: We screened 10 databanks and search engines, searched mesh and nonmesh terms. The identified evidences were reviewed and quality assessed using the Newcastle-Ottawa Scale (NOS). The heterogeneity between the primary results was investigated using Cochrane and I-square indices. Random effect model was applied to combine the standardized mean differences of thyroid function indicators between patients with and without melasma. P values meta-analysis was used to investigate the association between anti-TPO and melasma. RESULTS: We included seven studies, 473 cases, and 379 controls that had been investigated. The total standardized mean differences (95% confidence intervals) of TSH, T3, T4, and antithyroglobulin antibody between cases and controls were estimated to be 0.33 (0.18, 0.47), -0.01 (-0.20, 0.19), -1.50 (-2.96, -0.04), and 0.62 (0.14, 1.11), respectively. The corresponding figures among women were 0.35 (0.17, 0.52), 0.10 (-0.17, 0.38), -2.75 (-6.30, 0.81), and 0.99 (0.14, 1.83), respectively. P value of meta-analysis showed a significant relationship between anti-TPO serum level and melasma (Fisher = 26.80, P = 0.020). CONCLUSION: Serum levels of TSH, anti-TPO, and antithyroglobulin antibody were significantly higher in patients with melasma than those without melasma. Moreover, these differences were more severe among women with melasma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".