Outcomes in pediatric neurology: a review of conceptual issues and recommendationsThe 2010 Ronnie Mac Keith Lecture
Bibliographic record
Abstract
This paper discusses how to evaluate whether, and in what ways, treatments affect the lives of children with neurological conditions and their families. We argue that professionals should incorporate perspectives from patients and families to help them make decisions about what 'outcomes' are important, and we discuss how those outcomes might be assessed. A case vignette illustrates the differences and complementarity between the perspectives of clinicians and those of children and their parents. We recommend methods for expanding the range of relevant health outcomes in child neurology to include those that reflect the ways patients and families view their conditions and our interventions. We explore the added value of a 'non-categorical' approach to the choice of outcomes. The International Classification of Functioning, Disability and Health is a useful biopsychosocial framework to 'rule in' relevant aspects of child and family issues to create a dynamic system of possible influences on outcomes. We examine the meaning of 'health', 'health-related quality of life', and 'quality of life' as related but conceptually distinct outcomes. Specific issues are discussed about the construction, validation, and appraisal of outcome measures, as well as practical recommendations on how to select outcome measures in the clinical setting and research.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".