Prevalence of postpartum depression and anxiety among women with spinal cord injury
Bibliographic record
Abstract
Objective: To examine the prevalence of postpartum depression (PPD) and postpartum anxiety (PPA) in mothers with spinal cord injury (SCI).Design: Retrospective, cross-sectional study.Setting: Online multi-national study.Participants: We surveyed an international sample of 102 women who gave birth following cervical SCI (C1–C8, n = 30), upper thoracic SCI (T1–T6, n = 12) or lower level SCI (T7 & below, n = 60). Participants were primarily from Canada and Sweden, and mean age at childbirth was 30 ± 6 years.Outcome Measures: Subscales from the Pregnancy Risk Assessment Monitoring System (PRAMS) were used to measure PPD (PRAMS-3D) and PPA (PRAMS-2A).Results: PPD and PPA were most prevalent in women with cervical SCI, followed by upper thoracic SCI then lower SCI. Self-reported PPD was more prevalent than clinically diagnosed PPD in women with cervical SCI (P = 0.03) and upper thoracic SCI (P = 0.03). With cervical SCI, 75% of women diagnosed with MDD before pregnancy scored >9 on the PRAMS PPD subscale, indicating clinically relevant PPD. However, only 10% were diagnosed with PPD. Of women with lower SCI diagnosed with MDD before pregnancy, 25% had a clinically relevant score for self-reported PPD; 7% were diagnosed.Conclusions: This is currently the largest study examining PPD and PPA after SCI. Clinicians should be aware that mothers with SCI (particularly high-level SCI) may have increased risk of PPD and PPA. PPD is poorly understood in women with SCI and may even be underdiagnosed. SCI-related risk factors for PPD and PPA should be explored.
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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 | 0.000 | 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) | 0.001 | 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".