Opioid-use disorder and reported pain after spine surgery: Risk-group patterns in cognitive-appraisal processes in a longitudinal cohort study
Bibliographic record
Abstract
Background: As spinal disorders cause significant pain over an extended period, prolonged opioid use could lead to an increased risk of opioid-use disorder (OUD) over recovery. This study examined cognitive-appraisal processes as potential moderators of OUD-risk, after adjusting for demographic and clinical factors. Methods: -Short Form assessed cognitive-appraisal processes. Three self-report items on reported opioid use before and after surgery enabled dividing the sample into OUD risk groups, and patient medical records captured mention of presurgical opioid use and dependency concerns to validate our OUD risk classification. Regression models examined reported pain at 3 months, with independent variables of appraisal at presurgery, 3-months postsurgery, and change in appraisal; and OUD-risk group-by-appraisal interactions, after covariate adjustment. The Benjamini-Hochberg procedure reduced the false-discovery rate. Results: The OUD risk classification was validated. Baseline (presurgery) cognitive-appraisal processes moderated reported pain at 3-months postsurgery as a function of OUD risk in some areas. Notably, reported pain was lower among high-OUD risk patients who endorsed at presurgery more problem-resolution goals. In contrast, reported pain at 3 months was higher among low-OUD risk patients who endorsed at presurgery more problem-resolution goals. However, cognitive-appraisal processes at 3 months or change in appraisal did not moderate the relationship between OUD-risk group and pain. Conclusions: Cognitive-appraisal processes at presurgery moderated the OUD-risk groups' experience of pain at 3 months postsurgery. For high OUD-risk patients, more goals were associated with less pain, whereas the opposite was true for low-risk patients. The group differences for presurgery appraisal may be useful targets of early cognitive and mindfulness interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".