Pregnancy and Neonatal Outcomes With Levothyroxine Treatment in Women With Subclinical Hypothyroidism Based on New Diagnostic Criteria: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Subclinical hypothyroidism (SCH) during pregnancy has been associated with multiple adverse maternal and neonatal outcomes. However, the potential benefits of levothyroxine (LT4) supplementation remain controversial. Variations across studies in diagnostic criteria for SCH may, in part, explain the divergent findings on the subject. This study aimed to assess the effect of LT4 treatment on pregnancy and neonatal outcomes among pregnant women who were diagnosed as SCH based on the most recent diagnostic criteria. Methods: We conducted a systematic review and meta-analysis of the literature published from inception to January 2020. The search strategy targeted the studies on pregnancy and neonatal outcomes following LT4 treatment in women with SCH based on 2017 American Thyroid Association diagnostic criteria. Pooled effect sizes were estimated using fixed and random effect models, according to the absence or presence of heterogeneity which was assessed using the I-squared statistic. Sources of heterogeneity and the stability of results were evaluated through sensitivity analysis. Results: Of the 2781 identified references, 306 full-text articles were screened for eligibility. Finally, 6 studies including a total of 7955 participants were retained for analysis. Summary effect estimates indicated that pregnant women with SCH treated with LT4 had a lower risk of pregnancy loss [odds ratio (OR) = 0.55, 95% confidence interval (CI): 0.43-0.71], preterm birth (OR=0.63, 95% CI: 0.41-0.98) and gestational hypertension (OR = 0.78, 95% CI: 0.63-0.97) than those in control group. Conclusion: LT4 treatment in pregnant women with SCH may reduce the risk of pregnancy loss, preterm delivery and gestational hypertension.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.006 | 0.006 |
| 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) | 0.003 | 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".