Pain Mechanisms in Low Back Pain: A Systematic Review With Meta-analysis of Mechanical Quantitative Sensory Testing Outcomes in People With Nonspecific Low Back Pain
Bibliographic record
Abstract
Background Mechanical quantitative sensory testing (QST) assesses sensory functioning and detects functional changes in (central) nociceptive processing. It has been hypothesized that these functional changes might be apparent in people with nonspecific low back pain (LBP), although the results are mixed. Objective The aim of this systematic review was to examine whether sensory function, measured with QST, was altered in people with nonspecific LBP. Methods This systematic review was conducted according to PRISMA guidelines. Six databases were searched for relevant literature. Studies comparing mechanical QST measures involving people with subacute and chronic LBP and healthy controls were included if (1) pressure pain thresholds (PPTs), (2) temporal summation, or (3) conditioned pain modulation were reported. Risk of bias was assessed using the Newcastle-Ottawa scale. When possible, the results from different studies were pooled. Results Twenty-four studies were included. Scores on the Newcastle-Ottawa scale varied between 1 and 6 points. People with nonspecific LBP, compared to healthy controls, had significantly lower PPTs at remote sites and increased temporal summation at the lower back. The PPTs measured at the scapula were significantly lower in patients with nonspecific LBP than in healthy controls (pooled mean difference, 119.2 kPa; 95% confidence interval: 91.8, 146.6 kPa; P<.001). Conclusion The PPT measurements at remote body parts were significantly lower in people with nonspecific LBP compared with healthy controls. Temporal summation and conditioned pain modulation measurements had mixed outcomes. Level of Evidence Therapy, level 3a. J Orthop Sports Phys Ther 2019;49(10):698–715. Epub 23 Aug 2019. doi:10.2519/jospt.2019.8876
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".