Course of Back Pain in the Canadian Population: Trajectories, Predictors, and Outcomes
Bibliographic record
Abstract
OBJECTIVE: To identify and describe back pain trajectory groups and to compare indicators of health status, medication, and health care use in these groups. METHODS: A representative sample (n = 12,782) of the Canadian population was followed-up from 1994/1995 to 2010/2011. Participants were interviewed biannually and provided data on sociodemographic (e.g., education) and behavior-related (e.g., physical activity) factors, depression, comorbidities, pain, disability, medication use (e.g., opioids), and health care use (e.g., primary care visits). We used group-based trajectory analysis to categorize participants according to patterns in the course of their back pain during the 16-year follow-up period and compared indicators of pain, disability, medication, and health care use in the trajectory groups. RESULTS: A total of 45.6% of the participants reported back pain at least once during follow-up. Of those, we identified 4 trajectories: persistent (18.0%), developing (28.1%), recovery (20.5%), and occasional (33.4%). The persistent and developing groups were characterized as having pain that prevented activities, disability, depression, and comorbidities. There were significant differences in the patterns of medication and health care use across the groups, with a general trend of most to least health care and medication use in the persistent, developing, recovering, and occasional groups. Those in the recovery group had an increasing trajectory reflecting opioid and antidepressant use. CONCLUSION: Approximately 1 in 5 people with back pain experience a persistent pain trajectory with an associated increase in pain, disability, and health care use. Further research is needed to determine whether the groups identified represent different diagnoses, which may provide insight into the selection of stratified treatment and aid in designing early prevention and management strategies in the population.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 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".