Prevalence and predictors of anti‐infective use during pregnancy
Bibliographic record
Abstract
PURPOSES: (1) Measure the prevalence and trends of anti-infective drug use before, during, and after pregnancy; (2) to list the doses, classes, types, and indications for anti-infective use during pregnancy; and (3) to identify predictors associated with anti-infective drug use during pregnancy. METHODS: Retrospective cohort study within the Quebec Pregnancy Registry, which was created by the linkage of three administrative databases: RAMQ, Méd-Echo, and ISQ. Women were eligible if they were (1) continuously insured by the RAMQ drug plan for at least 12 months before the first day of gestation, during pregnancy and 12 months after the end of the pregnancy and (2) if they gave birth to a live born between January 1998 and December 2003. Ninety-seven thousand six hundred and eighty pregnant women met the eligibility criteria. Data were collected for systemic agents. Logistic regression models were used to quantify predictors of use. RESULTS: Prevalence of anti-infective use during pregnancy was 24.5%. Penicillins use increased compared to other classes. The most frequently diagnosed infections were respiratory and urinary tract infections. Predictors associated with use at the beginning of gestation were having > or =2 different prescribers [OR = 3.83 (95% confidence interval 95%CI: 3.3-4.3)], diagnosis of urinary [OR = 1.50 (95%CI: 1.3-1.8)], and respiratory tract infection [OR = 1.40 (95%CI: 1.2-1.6)] in the year before pregnancy. Visits to an obstetrician/gynecologist were protective for use [OR = 0.81 (95%CI: 0.67-0.97)]. CONCLUSION: Anti-infective use during pregnancy is prevalent. The oldest and safest agents are preferred.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".