Trajectories of pain intensity, pain catastrophizing, and pain interference in the perinatal and postpartum period
Bibliographic record
Abstract
Introduction: Chronic pain (pain >3 months) is a growing epidemic. Normal pregnancy may give rise to recurrent and sometimes constant pain for women. Women with worse pain symptoms are more likely to report symptoms of anxiety, depression, and/or insomnia during the perinatal period, which may impact labor and delivery outcomes. We examined the relationship between demographic and psychological predictors of pain throughout pregnancy and into the postpartum. Objectives: To examine trajectories of pain intensity, pain catastrophizing, and pain interference during pregnancy and the early postpartum, and associated sociodemographic predictors of trajectory membership. Methods: One hundred forty-two pregnant women were assessed at 4 time points for measures of pain intensity, pain catastrophizing, pain interference, and symptoms of insomnia, depression, and generalized anxiety. Women completed the first survey before 20 weeks' gestation and were reassessed every 10 weeks. Surveys were completed on average at 15 weeks', 25 weeks', and 35 weeks' gestation, and at 6-week postpartum. Using latent class mixed models, trajectory analysis was used to determine trajectories of pain intensity, pain catastrophizing, and pain interference. Results: A 1-class pain intensity model, 2-class pain catastrophizing model, and 3-class pain interference model were identified. Adaptive lasso and imputation demonstrated model robustness. Individual associations with trajectories included baseline symptoms of anxiety, depression, and insomnia, and pain symptomology. Conclusion: These findings may help to identify women who are at high risk for experiencing pain symptoms during pregnancy and could aid in developing targeted management strategies to prevent mothers from developing chronic pain during their pregnancy and into the postpartum period.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".