Association of Pain Catastrophizing and Depressive States with Multidimensional Early Labor Pain Assessment in Nulliparous Women Having Epidural Analgesia – A Secondary Analysis
Bibliographic record
Abstract
BACKGROUND: Labor pain is a variable and complex experience with both sensory and affective components. Pain catastrophizing tendencies are predictive of increased distress during labor. Likewise, pain severity has important associations with increased depressive symptoms in mothers, with consequences on perinatal and infant outcomes. Hence, we investigated the association between increased early labor pain with both pre-delivery pain catastrophizing and depressive states. METHODS: We recruited nulliparous women who had requested labor epidural analgesia. Pre-delivery questionnaires including short-form McGill pain questionnaire-2 (SF-MPQ-2), pain catastrophizing scale (PCS), and Edinburgh postnatal depression score (EPDS) were administered. RESULTS: A total of 712 women completed the pre-delivery questionnaires. There was a significant association between SF-MPQ-2 neuropathic subscale and EPDS ≥ 10 (unadjusted OR 1.74, 95% CI 1.11-2.73, p = 0.0161), as well as PCS ≥ 25 (unadjusted OR 1.55, 95% CI 1.06-2.26, p = 0.0244). SF-MPQ-2 sensory intermittent subscale and EPDS ≥ 10 (unadjusted OR 2.02, 95% CI 1.34-3.03, p = 0.0007), and PCS ≥ 25 (unadjusted OR 1.59, 95% CI 1.14-2.23, p = 0.0069) also showed significant association. CONCLUSION: Increased sensory intermittent and neuropathic subsets of early labor pain are significantly correlated with increased pre-delivery pain catastrophizing and depressive states in nulliparous women. This positive association may be useful for pre-delivery risk stratification for early interventions towards a more holistic care management.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".