In‐hospital opioid consumption, but not pain intensity scores, predicts 6‐month levels of pain catastrophizing following hepatic resection: A trajectory analysis
Bibliographic record
Abstract
BACKGROUND: The study aims were to model acute pain intensity and opioid consumption trajectories up to 72 hr after open hepatic resection, identify predictors of trajectory membership and examine the association between trajectory memberships and 6-month pain and psychological outcomes. This is a long-term analysis of a published randomized controlled trial on the impact of medial open transversus abdominis plane catheters on post-operative outcomes. METHODS: A total of 152 patients (89 males; mean age 63.0 [range: 54-72]) completed questionnaires on pain and related characteristics pre-operatively and 6 months post-operatively. Total opioid use was recorded several times over a 72-hr period while self-reported pain intensity scores were collected multiple times until hospital discharge. Analyses were carried out using growth mixture modelling, logistic regression and general linear models. RESULTS: Both pain intensity and opioid consumption showed that a four-trajectory model best fits the data. Patients in the lowest opioid consumption trajectory were more likely to be classified in the constant mild pain intensity trajectory. Age and baseline levels of anxiety significantly predicted opioid trajectory membership while baseline depressive symptoms significantly predicted pain intensity trajectory membership. Patients in the two highest opioid consumption trajectories reported significantly higher levels of pain catastrophizing at 6 months compared to patients in the other 3 trajectories (all p < 0.05). CONCLUSION: High consumption of opioids after surgery is associated with higher levels of pain catastrophizing 6 months later. Identification of patients within these trajectories may lead to the development of early interventions targeted to high risk individuals. SIGNIFICANCE: Differences in initial levels of opioid consumption and rates of change in opioid consumption shortly after surgery can help predict long-term psychological responses to pain. Identifying key characteristics associated with initial opioid consumption can lead to the development of cost-effective early interventions targeted to high risk individuals.
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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 | 0.004 | 0.006 |
| 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 | 0.001 |
| 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".