History of Concussion and Risk of Severe Maternal Mental Illness
Bibliographic record
Abstract
To evaluate the relationship between a predelivery history of concussion and risk of severe maternal mental illness. We conducted a population based cohort study of birthing people with a singleton livebirth accrued between 2007 and 2017 with follow-up to 2021 in Ontario, Canada. The primary outcome was severe maternal mental illness, defined as a psychiatric emergency department visit, psychiatric hospital admission, or self-harm or suicide in the 14 years after delivery. Cox proportional hazards regression generated adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) comparing those with a history of a health care encounter for concussion between database inception and the index delivery date to those without a recorded health care encounter for concussion, adjusted for maternal age, parity, neighborhood income quintile, rural residence, immigration status, chronic conditions, history of interpersonal violence, and history of mental illness. Results were also stratified by history of mental illness. There were n = 18,064 birthing people with a history of concussion and n = 736,689 without a history of concussion. Those with a history of concussion had an increased risk of severe maternal mental illness compared to those without this history (14.7 vs 7.9 per 1,000 person-years; aHR 1.25, 95% CI, 1.20-1.31). After stratification by predelivery history of mental illness, the association was strongest in individuals with no mental illness history (aHR 1.33, 95% CI, 1.23-1.44). These findings indicate the need for early identification and screening of birthing people with a history of concussion, as well as ongoing long-term supports using trauma informed approaches to prevent adverse psychiatric outcomes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".