Clusters of facilitatory and inhibitory conditioned pain modulation responses in a large sample of children, adolescents, and young adults with chronic pain
Bibliographic record
Abstract
Introduction: When investigating the role of facilitatory and inhibitory pain mechanisms such as conditioned pain modulation (CPM) and temporal summation of pain (TSP), it is important to take both into consideration in a single experimental model to provide the most information on subgroups of patients. Therefore, the objective of this study was to identify subgroups in a large population of pediatric patients with chronic pain based on their facilitatory and inhibitory pain mechanisms and compare them with control subjects. Methods: Five hundred twenty-one female subjects and 147 male subjects between 8 and 21 years old underwent a CPM assessment using a 2-minute tonic noxious heat stimulation as the test stimulus and a 2-minute cold-pressor task (CPT) (12°C) as the conditioning stimulus. Results: The best partition of clusters of patients was 3 clusters accounting for 27.15% of the total variation in the data. Cluster 1 (n = 271) was best characterized by high pain intensity during the CPT, lack of TSP during the test stimuli, and efficient inhibitory CPM. Cluster 2 (n = 186) was best characterized by low pain intensity during the CPT, lack of TSP during the test stimuli, and efficient inhibitory CPM. Cluster 3 (n = 151) was best characterized by high pain intensity during the CPT, presence of TSP during the test stimuli, and inefficient inhibitory CPM. Discussion: A single thermal CPM experimental design can identify combinations of facilitatory and inhibitory pain modulation responses. Findings from the current study add to the literature by describing different clinical phenotypes of central pain mechanisms of youth with 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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 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".