The nociceptive flexion reflex: a scoping review and proposed standardized methodology for acquisition in those affected by chronic pain
Bibliographic record
Abstract
The nociceptive flexion reflex (NFR) is used in neurophysiological research as an objective measure of nociception. NFR thresholds are reduced in numerous chronic pain pathologies, which are indicative of common central hyperexcitability within conditions. However, variation exists in both the NFR assessment and determinants of NFR threshold among research groups. Our purpose was to provide a review of the recent literature to (a) confirm the NFR threshold’s efficacy in identifying those with chronic pain compared to controls and (b) provide a narrative synthesis on the current methodology used to assess the NFR in clinical populations. We conducted a review of multiple databases (MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Google Scholar and Cochrane Library), including articles that reported controlled clinical studies of humans, in English, comparing NFR thresholds within chronic pain conditions to matched control subjects, published since the last NFR review in 2010. Our search resulted in nine studies included in our narrative synthesis and eight studies included in a meta-analysis. There was a significant pooled standardized mean difference in NFR threshold between chronic pain conditions and controls (−0.94, 95% confidence interval (CI) −1.33 to −0.55, p < 0.0001), with substantial heterogeneity of pooled estimates ( I 2 = 87%, τ 2 = 0.41, Q = 76.13, the degrees of freedom (df) = 11, p < 0.0001). Significant variations in participant positioning, stimulation parameters and determinants of the NFR threshold were evident among included studies. We provided a narrative synthesis on the methodologies of included studies, as a recommendation for future studies in the assessment of the NFR in chronic pain.
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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 | 0.112 | 0.227 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.042 | 0.030 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.007 | 0.004 |
| 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".