Research priorities for children with neurological impairment and medical complexity in high‐income countries
Bibliographic record
Abstract
AIM: To identify the highest-priority clinical research areas related to children with neurological impairment and medical complexity among clinicians and caregivers. METHOD: A modified, three-stage Delphi study using online surveys and guided by a steering committee was completed. In round 1, clinicians and family caregivers suggested clinical topics and related questions that require research to support this subgroup of children. After refinement of the suggestions by the steering committee, participants contributed to 1 (family caregivers) or 2 (clinicians) subsequent rounds to develop a prioritized list. RESULTS: A diverse international expert panel consisting of 49 clinicians and 12 family caregivers provided 601 responses. Responses were distilled into 26 clinical topics comprising 126 related questions. The top clinical topics prioritized for research were irritability and pain, child mental health, disorders of tone, polypharmacy, sleep, aspiration, behavior, dysautonomia, and feeding intolerance. The clinician expert panel also prioritized 10 specific research questions. INTERPRETATION: Study findings support a research agenda for children with neurological impairment and medical complexity focused on addressing clinical questions, prioritized by an international group of clinicians and caregivers.
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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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".