Pain Measurement in Infants and Children With and at Risk for Intellectual Disabilities
Bibliographic record
Abstract
Standards of patient care require that comprehensive pain assessments be conducted at routine intervals. Infants and children with and at risk for intellectual disabilities, who are at high risk for experiencing pain, receive significantly less representation in the literature to inform pain measurement practice. The objectives of this review include (1) review and discuss the current literature surrounding pain measurement in infants and children with and at risk for intellectual disabilities, (2) define pain assessment tools, scales, and measures that are being used in infants and children with and at risk for intellectual disabilities, (3) discuss the strengths and limitations of the pain assessment tools, scales, and measures, (4) make recommendations for future pain research focused on this population. A narrative review of the literature regarding pain measures in infants and children with and at risk for intellectual disabilities was conducted using PubMed. A search strategy was created in consultation with a librarian scientist. There were no date limiters applied to the search. Pain measures can be classified as self-report, behavioral (e.g., cry, facial expressions), physiological (e.g., heart rate, biomarkers, oxygen saturation, respiratory rate), and neurophysiological (electroencephalogram, functional magnetic resonance imaging, near infrared spectroscopy). There is a considerable dearth in the literature surrounding pain measures and pain indicators in this population, along with small sample sizes and inconsistent findings reported across studies. Future research is needed to compare pain responses across different age groups and intellectual disability diagnoses to neurotypical peers.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".