Nature and Determinants of the Course of Chronic Low Back Pain Over a 12-Month Period: A Cluster Analysis
Bibliographic record
Abstract
BACKGROUND: It has been suggested that low back pain (LBP) is a condition with an unpredictable pattern of exacerbation, remission, and recurrence. However, there is an incomplete understanding of the course of LBP and the determinants of the course. OBJECTIVE: The purposes of this study were: (1) to identify clusters of LBP patients with similar fluctuating pain patterns over time and (2) to investigate whether demographic and clinical characteristics can distinguish these clusters. DESIGN: This study was a secondary analysis of data extracted from a randomized controlled trial. METHODS: Pain scores were collected from 155 participants with chronic nonspecific LBP. Pain intensity was measured monthly over a 1-year period by mobile phone short message service. Cluster analysis was used to identify participants with similar fluctuating patterns of pain based on the pain measures collected over a year, and t tests were used to evaluate if the clusters differed in terms of baseline characteristics. RESULTS: The cluster analysis revealed the presence of 3 main clusters. Pain was of fluctuating nature within 2 of the clusters. Out of the 155 participants, 21 (13.5%) had fluctuating pain. Baseline disability (measured with the Roland-Morris Disability Questionnaire) and treatment groups (from the initial randomized controlled trial) were significantly different in the clusters of patients with fluctuating pain when compared with the cluster of patients without fluctuating pain. LIMITATIONS: A limitation of this study was the fact that participants were undergoing treatment that may have been responsible for the rather positive prognosis observed. CONCLUSIONS: A small number of patients with fluctuating patterns of pain over time were identified. This number could increase if individuals with episodic pain are included in this fluctuating group.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".