Medical treatment in pregnant women with subclinical hypothyroidism: systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Objectives: to determine the effectiveness of medical therapy in reducing complications associated with subclinical hypothyroidism during pregnancy. Methods: in 2021, a systematic review of available cohort studies was carried out in three databases, with no publication date limit. Study selection and data extraction were performed in duplicate. Random-effects meta-analysis was performed, and odds ratios were calculated, with the corresponding 95% confidence intervals. Cohort risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). The certainty of the evidence was assessed using the GRADE methodology. Results: five studies were included for qualitative and quantitative synthesis. A statistically significant relationship was found between medical treatment in pregnant women with subclinical hypothyroidism with respect to spontaneous abortion (p=0.03; OR=0.77; CI95%=0.61-0.97), and no statistically significant relationship was found for delivery preterm (p=0.46; OR=1.11; CI95%=0.85-1.44), nor for abrupt placentae (p=0.56; OR=1.60; CI95%=0.33-7.66). Three studies were at moderate risk of bias, and two were at low risk of bias. In all the results the certainty was very low. Conclusions: medical treatment of subclinical hypothyroidism during pregnancy can have a beneficial effect in reducing cases of spontaneous abortion.
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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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".