Antidepressant use during pregnancy and the risk of gestational diabetes mellitus: a nested case–control study
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to determine the association between antidepressant (AD) classes, types and duration of use during pregnancy and the risk of gestational diabetes mellitus (GDM). DESIGN AND SETTING: A nested case-control study was conducted within the Quebec Pregnancy Cohort (QPC), a Canadian provincial database which includes data on all pregnancies and children in Quebec from January 1998 to December 2015. PRIMARY OUTCOME MEASURES: Gestational diabetes mellitus. PARTICIPANTS: Cases of GDM were identified after week 20 of pregnancy and randomly matched 1:10 to controls on gestational age at index date (ie, calendar date of GDM) and year of pregnancy. AD exposure was assessed by filled prescriptions between the beginning of pregnancy (first day of last menstrual period) and index date. Conditional logistic regression models were used to estimate crude and adjusted odds ratios (aOR). RESULTS: Among 20 905 cases and 209 050 matched controls, 9741 (4.2%) women were exposed to ADs. When adjusting for potential confounders, AD use was associated with an increased risk of GDM (aOR 1.19, 95% CI 1.08 to 1.30); venlafaxine (aOR 1.27, 95% CI 1.09 to 1.49) and amitriptyline (aOR 1.52, 95% CI 1.25 to 1.84) were also associated with an increased risk of GDM. Moreover, the risk of GDM was increased with longer duration of AD use, specifically for serotonin norepinephrine reuptake inhibitors, tricyclic ADs and combined use of two AD classes. No statistically significant association was observed for selective serotonin reuptake inhibitors. CONCLUSION: The findings suggest that ADs-and specifically venlafaxine and amitriptyline-were associated with an increased risk of GDM.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".