The Impact of Levothyroxine in Women with Positive Thyroid Antibodies on Pregnancy Outcomes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Thyroid autoimmunity in pregnant women has been associated with negative outcomes. Objective: To evaluate the effect of levothyroxine therapy on pregnancy outcomes compared with placebo or no treatment in women without overt hypothyroidism who are TPOAb and/or TgAb-positive. Search Strategy: Ovid MEDLINE, EMBASE, CINAHL, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials were searched from 1980 to April 17, 2019. Selection Criteria: Pre-specified criteria for inclusion were: randomized trials of levothyroxine versus control (placebo or no treatment) among women with positive TPOAb or TgAb who were pregnant or considering conception. Data Collection and Analysis: Pre-specified data elements were extracted and where appropriate, meta-analyses were conducted. Main outcomes include pregnancy achieved, miscarriage, preterm delivery and live birth. Main Results: From 2,812 citations, 79 citations were identified for full text review. Of these, six trials (total of 2,263 women) were included for qualitative and quantitative analyses. Risk of bias was deemed low for only one trial. There was no significant difference in the relative risk (RR) of pregnancy achieved (RR 1.03; 95% CI 0.93, 1.13), miscarriage (RR 0.93; 95% CI 0.76, 1.14), preterm delivery (RR 0.66; 95% CI 0.39, 1.10), or live births (RR 1.01; 95% CI 0.89, 1.16) in thyroid autoimmune women treated with levothyroxine compared to controls. Conclusion: Among pregnant women or women planning conception, with thyroid autoimmunity, there is a lack of evidence of benefit for levothyroxine use. Recommendations to use levothyroxine in this setting need to be reconsidered.
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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 | 0.019 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".