A population-based cohort study of perinatal mental illness following traumatic brain injury
Bibliographic record
Abstract
AIMS: To examine the risk of perinatal mental illness, including new diagnoses and recurrent use of mental healthcare, comparing women with and without traumatic brain injury (TBI), and to identify injury-related factors associated with these outcomes among women with TBI. METHODS: We conducted a population-based cohort study in Ontario, Canada, of all obstetrical deliveries to women in 2012-2021, excluding those with mental healthcare use in the year before conception. The cohort was stratified into women with no remote mental illness history (to identify new mental illness diagnoses between conception and 365 days postpartum) and those with a remote mental illness history (to identify recurrent illnesses). Modified Poisson regression generated adjusted relative risks (aRRs) (1) comparing women with and without TBI and (2) according to injury-related variables (i.e., number, severity, timing, mechanism and intent) among women with TBI. RESULTS: = 786,317 without a history of TBI (mean age: 30.6 years [SD, 5.0]). Women with TBI were at elevated risk of a new mental illness diagnosis in the perinatal period compared to women without TBI (18.5% vs. 12.7%; aRR: 1.31, 95% confidence interval [CI]: 1.24-1.39), including mood and anxiety disorders. Women with a TBI were also at elevated risk for recurrent use of mental healthcare perinatally (35.5% vs. 27.8%; aRR: 1.18, 95% CI: 1.14-1.22), including mood and anxiety, psychotic, substance use and other mental health disorders. Among women with a history of TBI, the number of TBI-related healthcare encounters was positively associated with an elevated risk of new-onset mental illness. CONCLUSIONS: These findings demonstrate the need for providers to be attentive to the risk for perinatal mental illness in women with a TBI. This population may benefit from screening and tailored mental health supports and treatment options.
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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".