Somatosensory Testing in Pediatric Patients with Chronic Pain: An Exploration of Clinical Utility
Bibliographic record
Abstract
We aimed to evaluate the utility of clinical somatosensory testing (SST), an office adaptation of laboratory quantitative sensory testing, in a biopsychosocial assessment of a pediatric chronic somatic pain sample (N = 98, 65 females, 7-18 years). Stimulus-response tests were applied at pain regions and intra-subject control sites to cutaneous stimuli (simple and dynamic touch, punctate pressure and cool) and deep pressure stimuli (using a handheld pressure algometer, and, in a subset, manually inflated cuff). Validated psychological, pain-related and functional measures were administered. Cutaneous allodynia, usually regional, was elicited by at least one stimulus in 81% of cases, most frequently by punctate pressure. Central sensitization, using a composite measure of deep pressure pain threshold and temporal summation of pain, was implied in the majority (59.2%) and associated with worse sleep impairment and psychological functioning. In regression analyses, depressive symptoms were the only significant predictor of pain intensity. Functional interference was statistically predicted by deep pressure pain threshold and depressive symptoms. Manually inflated cuff algometry had comparable sensitivity to handheld pressure algometry for deep pressure pain threshold but not temporal summation of pain. SST complemented standard biopsychosocial assessment of pediatric chronic pain; use of SST may facilitate the understanding of disordered neurobiology.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".