Predicting drug efficacy in chronic low back pain by quantitative sensory tests
Bibliographic record
Abstract
BACKGROUND: Drugs are prescribed for chronic low back pain without knowing in advance whether a patient will respond to them or not. Quantitative sensory tests (QST) can discriminate patients according to sensory phenotype, possibly reflecting underlying mechanisms of pain processing. QST may therefore be a screening tool to identify potential responders to a certain drug. The aim of this study was to investigate whether QST can predict analgesic effects of oxycodone, imipramine and clobazam in chronic low back pain. METHODS: Oxycodone 15 mg (n = 50), imipramine 75 mg (n = 50) and clobazam 20 mg (n = 49) were compared to active placebo tolterodine 1 mg in a randomized, double-blinded, crossover fashion. Electrical, pressure and thermal QST were performed at baseline and after 1 and 2 h. Pain intensity was assessed on a 0-10 numeric rating scale every 30 min for up to 2 h. The ability of baseline QST to predict pain reduction after 2 h was analysed using linear mixed models. Genetic variants of drug-metabolizing enzymes and genes affecting pain sensitivity were examined as covariables. RESULTS: No predictor of analgesic effect was found for oxycodone and clobazam. Thermal QST was associated with analgesic effect of imipramine: patients more sensitive to heat or cold were more likely to experience an effect of imipramine. Pharmacogenetic variants and pain-related candidate genes were not associated with drug efficacy. CONCLUSIONS: Thermal QST have the potential to predict imipramine effect in chronic low back pain. Oxycodone and clobazam effects could not be predicted by any of the selected QST or genetic variants. SIGNIFICANCE: Predicting drug efficacy in chronic low back pain remains difficult. There is some evidence that patients more sensitive to heat and cold pain respond better to imipramine.
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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 | 0.010 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".