Modeling Temporal Summation and Conditioned Pain Modulation in Individuals With and Without Chronic Pain
Bibliographic record
Abstract
OBJECTIVES: Although significant progress has been made in recent years in the field of pain modulation, information regarding patients' pain modulation profiles remains largely research-bound and is not yet easily accessible in clinical settings. The aim of this study was to develop and validate a model for estimating pain modulation profiles-including temporal summation of pain (TSP) and conditioned pain modulation (CPM)-using easily accessible measures. METHODS: This study included 347 pain-free individuals and 108 with chronic pain. TSP was induced through tonic heat pain stimulations, whereas CPM was evaluated using pressure pain thresholds (CPM-PPT) and tonic heat pain stimulations (CPM-HPS) as test stimuli, with the cold pressor test as the conditioning stimulus. Independent variables included demographic, psychological, and physiological measures. A LASSO regression with cross-validation was used to identify key independent. RESULTS: For TSP, the model explained 40% of the variance, incorporating factors such as monoamines and blood pressure. For CPM, the CPM-HPS model performed best, accounting for 35% of the variance, with blood pressure, sex, and pain catastrophizing identified as important predictors. DISCUSSION: Overall, these results indicate that TSP and CPM can be partially estimated using readily accessible measures, but high prediction error currently limits their clinical applicability.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".