Trajectories of Pain Intensity Over 1 Year in Adults With Disabling Subacute or Chronic Neck Pain
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to describe the 1-year trajectories of disabling subacute or persistent neck pain and to investigate whether baseline age, sex, pain characteristics, and depressive symptoms are associated with such trajectories. STUDY DESIGN AND SETTING: Participants (n=617) included in a randomized controlled trial provided weekly pain intensity ratings by responding to text messages over 1 year. We used latent class mixed model analyses to identify clusters of individual trajectories. Thereafter, we used logistic regression to determine the association between baseline age, sex, pain characteristics, depressive symptoms and treatment, and trajectories of neck pain. RESULTS: Six different clusters of trajectories were identified. Most participants (73%) followed a trajectory of decreasing pain throughout follow-up. The remaining experienced unfavorable trajectories: persistent pain of high intensity (22%) and slightly (3%) or highly (2%) fluctuating levels of pain reaching high levels of pain intensity. Pain intensity at baseline: odds ratio (OR): 3.76 (95% confidence interval [CI]: 2.49-5.68), depressive symptoms: OR: 3.46 (95% CI: 2.01-5.95), younger age: OR: 2.29 (95% CI: 1.48-3.54), female sex: OR: 1.51 (95% CI: 1.01-2.26), and sudden onset of pain: OR: 1.74 (95% CI: 1.13-2.69) were associated with unfavorable trajectories. CONCLUSIONS: Most individuals with disabling subacute or chronic neck pain show improvement in pain intensity over a year. However, a quarter present unfavorable trajectories. High pain intensity at baseline, depressive symptoms, younger age, female sex, and sudden onset of pain are factors associated with unfavorable trajectories.
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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 | 0.017 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".