Bibliographic record
Abstract
Pregnancy causes diverse physiologic and lifestyle changes that may contribute to increased driving and driver error. We compared a woman’s risk of a serious motor vehicle crash during her second trimester to her own baseline risk before pregnancy. We conducted a population-based self-matched exposure-crossover longitudinal cohort analysis of women who gave birth in Ontario, Canada, between April 1, 2006 and March 31, 2011 (5 years). We excluded women less than age 18 years, individuals living outside Ontario, those who lacked a valid identifier number under universal insurance, and cases managed by a midwife. The primary outcome was a motor vehicle crash resulting in a hospital emergency department visit. A total of 507,262 women gave birth during the study (mean age = 30 years, middle-low socioeconomic status = 64%, cesarean section rate = 30%). The women accounted for a total of 6,922 motor vehicle crashes as drivers during the three-year baseline interval (177 per month) and 757 motor vehicle crashes as drivers during their second trimester (252 per month). The elevated risk during the middle of pregnancy equaled a 42% increase in crash risk (95% confidence interval 32 to 53, p < 0.001). The increased risk included diverse populations, varied obstetrical cases, and different crash characteristics. The increased risk was largest in the early second trimester and compensated during the third trimester. No increase was observed in incidents involved as passengers or pedestrians, cases of intentional injury or inadvertent falls, or self-reported risky behaviors. The absolute risk amounted to an estimated 1-in-50 women experiencing a motor vehicle crash at some point during an average pregnancy, taking into account all nine months and the full spectrum of severity (fatal, injury, and vehicle damage combined). We suggest that pregnancy is associated with an increased risk of a serious motor vehicle crash during the second trimester that may merit attention in prenatal care guidelines.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".