Weight‐based thyroid dosing vs fixed dosing during pregnancy for subclinical hypothyroidism: A retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: Thyroid hormones play a crucial role in foetal growth and neurocognitive development. Our aim was to compare a weight-based dosing method of starting thyroxine to a fixed-dose method in newly diagnosed women with subclinical hypothyroidism during pregnancy. DESIGN: We performed a retrospective cohort study of consecutive women with newly diagnosed subclinical hypothyroidism during pregnancy seen at Mount Sinai Hospital and Women's College Hospital, Toronto, Canada 2015-2018. PATIENTS: We identified women that were treated based on pre-pregnancy weight and those that were given a fixed dose of 50 mcg/day. MEASUREMENTS: The percent of women who reached the target TSH of <2.5 mIU/L within 4-8 weeks was compared using a chi-squared test and a logistic regression model, adjusting for age, initial TSH and gestational age treatment was started. RESULTS: 393 women were included: 252 treated using a fixed-dose approach; 141 treated based on pre-pregnancy weight. In the unadjusted analysis, there was no difference between the groups in the percentage of women in the target range within 4-8 weeks (89.6% in the fixed-dose group vs 88.8% in the weight-based group (p = .954)). However, after adjustment for between-group differences in age, initial TSH and gestational age treatment was started, there was a significantly greater odds of achieving the target range using the weight-based dosing (OR 4.26 (1.60-11.7), p = .004). CONCLUSIONS: Treating women with newly diagnosed subclinical hypothyroidism during pregnancy with a weight-based strategy increased the odds of reaching the target TSH range within 4-8 weeks. Clinicians caring for these women should consider this approach when starting treatment during pregnancy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".