Quantitative sensory tests fairly reflect immediate effects of oxycodone in chronic low-back pain
Bibliographic record
Abstract
INTRODUCTION: Quantitative sensory tests (QST) can be used for profiling anti-nociceptive effects of analgesics. However, anti-nociceptive effects detected by QST are not necessarily associated with analgesic effects in pain patients. As part of a large investigation on low back pain, this paper describes the immediate analgesic and anti-nociceptive effects of oxycodone in chronic low-back pain and ranks different QST according to their ability to reflect this effect. The results are expected to support the selection of QST for future studies on potential novel opioid agonists in human pain. METHODS: In this randomized, placebo-controlled and double-blinded cross-over study, 50 patients with chronic low-back pain received a single oral dose of oxycodone 15mg or active placebo, and underwent multiple QST testing. The intensity of low-back pain was recorded during 2h. The areas under the ROC curves and 95% confidence intervals were determined, whereby responder status (≥30% pain reduction) was set as reference variable and changes in QST from baseline were set as classifiers. RESULTS: Significant analgesic effect on low-back pain as well as anti-nociceptive effects for almost all QST parameters were observed. The QST with the highest area under the curve were heat pain detection threshold (0.65, 95%-CI 0.46 to 0.83), single-stimulus electrical pain threshold (0.64, 95%-CI 0.47 to 0.80) and pressure pain detection threshold (0.63, 95%-CI 0.48 to 0.79). CONCLUSIONS: The results suggest that anti-nociceptive effects assessed by QST fairly reflect clinical efficacy of oxycodone on low-back pain. Pressure pain detection threshold, heat pain detection threshold and single-stimulus electrical pain threshold may be more suitable to sort out potential non-responders rather than identifying potential responders to opioid medication. Future pre-clinical human research may consider these results when investigating the analgesic effect of opioid agonists by means of QST.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".