Cold pain hypersensitivity predicts trajectories of pain and disability after low back surgery: a prospective cohort study
Bibliographic record
Abstract
Improving the ability to predict persistent pain after spine surgery would allow identification of patients at risk and guide treatment decisions. Quantitative sensory tests (QST) are measures of altered pain processes, but in our previous study, preoperative QST did not predict pain and disability at single time-points. Trajectory analysis accounts for time-dependent patterns. We hypothesized that QST predict trajectories of pain and disability during 1 year after low back surgery. We performed a trajectory analysis on the cohort of our previous study (n = 141). Baseline QST included electrical, pressure, heat, and cold stimulation of the low back and lower extremity, temporal summation, and conditioned pain modulation. Pain intensity and Oswestry Disability Index were measured before, and 2, 6, and 12 months after surgery. Bivariate trajectories for pain and disability were computed using group-based trajectory models. Multivariable regressions were used to identify QST as predictors of trajectory groups, with sociodemographic, psychological, and clinical characteristics as covariates. Cold pain hypersensitivity at the leg, not being married, and long pain duration independently predicted worse recovery (complete-to-incomplete, incomplete-to-no recovery). Cold pain hypersensitivity increased the odds for worse recovery by 3.8 (95% confidence intervals 1.8-8.0, P < 0.001) and 3.0 (1.3-7.0, P = 0.012) in the univariable and multivariable analyses, respectively. Trajectory analysis, but not analysis at single time-points, identified cold pain hypersensitivity as strong predictor of worse recovery, supporting altered pain processes as predisposing factor for persisting pain and disability, and a broader use of trajectory analysis. Assessment of cold pain sensitivity may be a clinically applicable, prognostic test.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.001 | 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".