Individualized Numeric Rating Scale to Assess Pain in Critically Ill Children With Neurodevelopmental Disabilities
Bibliographic record
Abstract
BACKGROUND: Pain is a significant burden for children with neurodevelopmental disabilities but is difficult for clinicians to identify. No pain assessment tools for children with neurodevelopmental disabilities have been validated for use in pediatric intensive care units. The Individualized Numeric Rating Scale (INRS) is an adapted 0-to-10 rating that includes parents' input on their child's pain indicators. OBJECTIVES: To evaluate the reliability, validity, and feasibility and acceptability of use of the INRS for assessing pain in critically ill children with neurodevelopmental disabilities. METHODS: This observational study enrolled critically ill patients with neurodevelopmental disabilities aged 3 to 17 years in 2 pediatric intensive care units at a children's hospital using a prospective repeated-measures cohort design. Structured parent interviews were used to populate each patient's INRS. Bedside nurses assessed pain using the INRS throughout the study. The research team completed independent INRS ratings using video clips. Participating parents and nurses completed feasibility and acceptability surveys. Psychometric properties of the INRS and survey responses were evaluated with appropriate statistical methods. RESULTS: For 481 paired INRS pain ratings in 34 patients, interrater reliability between nurse and research team ratings was moderate (weighted κ = 0.56). Parents said that creating the INRS was easy, made them feel more involved in care, and helped them communicate with nurses. CONCLUSIONS: The INRS has adequate measurement properties for assessing pain in critically ill children with neurodevelopmental disabilities. It furthers goals of patient- and family-centered care but may have implementation barriers.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".