Distinguishing problematic from nonproblematic postsurgical pain
Bibliographic record
Abstract
The goal of this study was to follow a cohort of patients undergoing total knee arthroplasty over time to: (1) identify and describe the various pain trajectories beginning preoperatively and for up to 12 months after surgery, (2) identify baseline predictors of trajectory group membership, and (3) identify trajectory groups associated with poor psychosocial outcomes 12 months after surgery. One hundred seventy-three participants (female = 85 [49%]; mean age [years] = 62.9, SD = 6.8) completed pain and psychological questionnaires and functional performance tests preoperatively and 4 days, 6 weeks, and 3 and 12 months after total knee arthroplasty. Using growth mixture modeling, results showed that a 4-group model, with a quadratic slope and baseline pain data predicting trajectory group membership, best fit the data (Akaike information criterion = 2772.27). The first 3 pain trajectories represent various rates of recovery ending with relatively low levels of pain 12 months after surgery. Group 4, the constant high pain group, comprises patients who have a neutral or positive pain slope and do not show improvement in their pain experience over the first year after surgery. This model suggests that preoperative pain levels are predictive of pain trajectory group membership and moderate preoperative pain, as opposed to low or high pain, is a risk factor for a neutral or positive pain trajectory postoperatively. Consistent with previous studies, these results show that postoperative pain is not a homogeneous condition and point to the importance of examining intraindividual pain fluctuations as they relate to pain interventions and prevention strategies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".