Quantitative sensory testing for assessment of somatosensory function in children and adolescents: a scoping review
Bibliographic record
Abstract
Quantitative sensory testing (QST) refers to a group of noninvasive psychophysical tests that examine responses to a range of calibrated mechanical and thermal stimuli. Quantitative sensory testing has been used extensively in adult pain research and has more recently been applied to pediatric pain research. The aims of this scoping review were to map the current state of the field, to identify gaps in the literature, and to inform directions for future research. Comprehensive searches were run in 5 databases. Titles, abstracts, and full texts were screened by 2 reviewers. Data related to the study aims were extracted and analyzed descriptively. A total of 16,894 unique studies were identified, of which 505 were screened for eligibility. After a full-text review, 301 studies were retained for analysis. Date of publication ranged from 1966 to 2023. However, the majority of studies (61%) were published within the last decade. Studies included participants across the developmental trajectory (ie, early childhood to adolescence) and most often included a combination of school-age children and adolescents (49%). Approximately 23% of studies were conducted in healthy samples. Most studies (71%) used only one QST modality. Only 14% of studies reported using a standardized QST protocol. Quantitative sensory testing in pediatric populations is an emerging and rapidly growing area of pain research. Future work is needed using comprehensive, standardized QST protocols to harness the full potential that this procedure can offer to our understanding of pediatric 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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".