Association between the Use of Quantitative Sensory Testing and Conditioned Pain Modulation and the Prescription of Medication and Interventional Procedures in Children with Chronic Pain Conditions
Bibliographic record
Abstract
The evidence supporting the use of pharmacological treatments in pediatric chronic pain is limited. Quantitative sensory testing (QST) and conditioned pain modulation evaluation (CPM) provide information on pain phenotype, which may help clinicians to tailor the treatment. This retrospective study aimed to evaluate the association between the use of QST/CPM phenotyping on the selection of the treatment for children with chronic pain conditions. We retrospectively analyzed the medical records of 208 female patients (mean age 15 ± 2 years) enrolled in an outpatient interdisciplinary pediatric complex pain center. Pain phenotype information (QST/CPM) of 106 patients was available to the prescribing physician. The records of 102 age- and sex-matched patients without QST/CPM were used as controls. The primary endpoint was the proportion of medications and interventions prescribed. The secondary endpoint was the duration of treatment. The QST/CPM group received less opioids (7% vs. 28%, respectively, p < 0.001), less anticonvulsants (6% vs. 25%, p < 0.001), and less interventional treatments (29% vs. 44%, p = 0.03) than controls. Patients with an optimal CPM result tended to be prescribed fewer antidepressants (2% vs. 18%, p = 0.01), and patients with signs of allodynia and/or temporal summation tended to be prescribed fewer NSAIDs (57% vs. 78%, p = 0.04). There was no difference in the duration of the treatments between the groups. QST/CPM testing appears to provide more targeted therapeutic options resulting in the overall drop in polypharmacy and reduced use of interventional treatments while remaining at least as effective as the standard of care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 |
| 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".