Predicting transition from acute to chronic low back pain with quantitative sensory tests—A prospective cohort study in the primary care setting
Bibliographic record
Abstract
BACKGROUND: It would be desirable to identify patients with acute low back pain (ALBP) who are at high risk for transition to chronic pain early in the course of their disease. This would enable early preventive or therapeutic interventions. Patients with chronic low back pain (CLBP) display signs of central hypersensitivity. This may contribute to the transition to CLBP. We tested the hypothesis that central hypersensitivity as assessed by quantitative sensory tests predicts transition to CLBP. METHODS: We performed a prospective cohort study in 130 patients with ALBP recruited in a primary care setting to determine the ability of 14 tests using electrical, pressure and temperature stimulation to predict transition to CLBP after 6 months. We assessed the association of tests with transition to CLBP in multivariable analyses adjusted for socio-demographic, psychological and clinical characteristics, quantified the performance of tests using receiver operating characteristic (ROC) curves, and calculated likelihood ratios for different cut-off values for most promising tests. RESULTS: None of the evaluated tests showed a statistically significant or clinically relevant ability to predict the transition to CLBP, with 95% CI of crude and adjusted associations of all tests including one as measure of no association. Corresponding estimates of areas under the ROC curves were below 0.5, and none of the 95% CI crossed the pre-specified boundary of clinical relevance set at 0.70. CONCLUSIONS: We found no evidence to support a clinically relevant ability of current quantitative sensory tests to predict the transition from acute to CLBP.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 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".