Insights From the International Consensus on Neuro-Irritability in Pediatric Palliative Care
Bibliographic record
Abstract
Background and Objectives: Many children with severe neurologic impairment (SNI) have complex care coordination and management needs, making them eligible for pediatric palliative care (PPC). Indeed, SNI is associated with numerous, often simultaneous, distressing symptoms. Among these, neuro-irritability is particularly common and impactful. The limited understanding of this condition poses a challenge for clinicians in terms of recognition and management, constituting a significant barrier to adequate care. Given the lack of scientific evidence and clinical practice guidelines, we initiated an international and multidisciplinary project to obtain consensus-based guidance on pediatric neuro-irritability. Methods: A panel of 66 experts from across the world was selected to participate in a 2-round Delphi method. The aim of this process was to gather their opinions and insights in the areas of Definition, Assessment, Monitoring, and Treatment of neuro-irritability as encountered in PPC. Panelists were asked to indicate their level of agreement with a series of statements through an online survey, using a 5-point Likert scale. Consensus on a particular item was defined as achieving ≥75% of (dis)agreement among participants. Results: A total of 55 statements were endorsed during the 2 voting sessions. In addition to defining the fundamental features of neuro-irritability, several core elements of the diagnostic process and follow-up protocol were developed. Recommendations on the pharmacological and nonpharmacological approaches are also provided. Discussion: This first international expert consensus aims to support physicians in identifying and addressing neuro-irritability in children with SNI. Our work provides a framework to enhance knowledge on neuro-irritability and advance clinical practice, identifying unmet needs and areas of uncertainty that warrant further research efforts.
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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 | 0.180 | 0.165 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".