Quantitative sensory testing and exercise-induced hypoalgesia protocols in low back pain: A scoping review
Bibliographic record
Abstract
A significant driver of pain in individuals with low back pain (LBP) is alterations to endogenous pain modulation (EPM). EPM can be measured using quantitative sensory testing (QST), however; there are inconsistencies in the way QST has been implemented across the low back pain literature. The objective of this scoping review was to summarize protocols used to assess EPM using QST (pain pressure threshold (PPT), temporal summation (TS), conditioned pain modulation (CPM)) or exercise-induced hypoalgesia (EIH) in LBP. Databases Medline, Embase, CINAHL and AMED were searched on June 15, 2023, for articles that used QST or EIH protocols in LBP populations. Data was extracted on participants, study design, setting and details on QST and EIH protocols. Of the 221 studies included in the review, 196 used PPT, 62 used TS and 60 used CPM; only 5 studies investigated EIH. For all QST, there was high variability in the type of equipment, timing, trials, and testing location with many studies not reporting this information. There were 4 testing modalities used for TS, and 7 different test stimuli, and 3 different conditioning stimuli used across the studies for CPM. For CPM and EIH, PPT was the most common testing modality. There were 4 types of exercises used across the 5 EIH studies. This scoping review provides a summary of QST and EIH protocols in LBP that may be used as a guide for assessment in future studies. These results demonstrate a need for the development of standardized protocols and reporting guidelines. PERSPECTIVE: This article presents a summary of measures used to assess EPM in LBP. The results show the wide variability of protocols used in the literature. Future research should focus on creating standardized protocols, reporting guidelines and providing more guidance for researchers in selecting appropriate tests for their research questions.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".