Associations between postpartum pain type, pain intensity and opioid use in patients with and without opioid use disorder: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Pain is a multidimensional construct. The purpose of this cross-sectional, single-centre study was to evaluate the relationship between postpartum pain type with pain intensity and opioid use in people with and without opioid use disorder (OUD). METHODS: Postpartum pain type was coded from McGill Pain Questionnaire and Patient-Reported Outcome Measurement Information System (PROMIS) inventories in people with or without OUD after childbirth in a 4-month period. The co-primary outcomes were pain intensity (0-10 scale) and total inpatient oxycodone (mg). Multivariable linear mixed-effects models assessed between- and within-person relationships for pain type (primary predictor) and outcomes. RESULTS: There were 44 522 unique pain scores and types from 2610 people. Pain types were associated with pain intensity (P<0.001). Between-person comparisons showed affective pain was associated with a small but higher total oxycodone dose (difference 1.04 mg compared with no affective pain, P<0.001). Among people with OUD, within-person comparisons showed that the presence of affective pain resulted in pain scores 1 point higher than when affective pain was not present (P=0.002); between-person comparisons showed that people with affective pain had pain scores 6 points higher (P=0.048). Within-person and between-person comparisons among OUD showed that nociceptive/neuropathic pain was associated with a higher total oxycodone dose (1.6 and 11.4 mg, respectively). CONCLUSIONS: Postpartum pain type was associated with pain intensity and opioid use. Further research is required to address the multiple dimensions of postpartum pain in people with and without OUD to improve treatment of postpartum pain.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".