Impact of different acute low back pain definitions on the predictors and on the risk of transition to chronic low back pain: a prospective longitudinal cohort study
Bibliographic record
Abstract
ABSTRACT: Inconsistencies in the identification of predictors for the transition from acute low back pain (aLBP) to chronic LBP (cLBP) may be attributed to the varying definitions of aLBP used in different studies. We investigated how adopting different aLBP definitions affects the set of predictors and the risk of transition to cLBP (LBP > 3 months that caused a problem for at least half the days in the past 6 months). We leveraged data from the ongoing prospective Quebec Low Back Pain Study to compose 3 aLBP groups at baseline: nonchronic (individuals not meeting the cLBP criteria, n = 788), acute (LBP < 3 months, n = 230), and new episode (LBP < 3 months preceded by ≥3 pain-free months, n = 182). The primary outcome was the transition to cLBP at 6 months. We built predictive models within groups using the minimum redundancy maximum relevance algorithm to identify key predictors, focusing on models discrimination and calibration. Risks of transition were 35.8%, 44.3%, and 45.6%, for the nonchronic, acute, and new episode groups, respectively. Pain intensity, disability, and depression emerged as consistent predictors across definitions. The acute and new episode models, but not the nonchronic , were considered clinically useful (area under the receiver operating characteristic curve > 0.7), with the latter displaying better calibration and increased performance after adjustment to pain duration. These findings highlight the importance of standardizing aLBP definitions to improve risk stratification and targeted early interventions. Clearer definitions can enhance predictive accuracy, ensuring more effective resource allocation and preventive strategies for individuals at risk of developing chronic pain.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".