A Practical Guide and Perspectives on the Use of Experimental Pain Modalities with Children and Adolescents
Bibliographic record
Abstract
Use of experimental pain is vital for addressing research questions that would otherwise be impossible to examine in the real world. Experimental induction of pain in children is highly scrutinized given the potential for harm and lack of direct benefit to a vulnerable population. However, its use has critically advanced our understanding of the mechanisms, assessment and treatment of pain in both healthy and chronically ill children. This article introduces various experimental pain modalities, including the cold pressor task, the water load symptom provocation test, thermal pain, pressure pain and conditioned pain modulation, and discusses their application for use with children and adolescents. It addresses practical implementation and ethical issues, as well as the advantages and disadvantages offered by each task. The incredible potential for future research is discussed given the array of experimental pain modalities now available to pediatric researchers.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".