Severe neurological impairment: a review of the definition
Bibliographic record
Abstract
Severe neurological impairment (SNI) is a term commonly used in the medical literature, though there is no agreed definition. This limits opportunities for research into healthcare needs, treatment opportunities, resource planning, and outcome. We reviewed the literature to establish consistency of use of the term and to place it in the context of other commonly employed terms used to describe children with severe, complex medical needs. Forty-two articles were included for full-text analysis, with 23 including a definition of SNI. Motor impairment, intellectual disability, communication difficulties, and increased care needs were included in the definition in 80%, 70%, 30%, and 13% of papers respectively. Dependence on others for decision-making, chronicity, and distinction between disorders of the central nervous system and peripheral nervous system were less frequently included. There is wide variation in the use of the term SNI. A consensus-based definition of this term would be useful to facilitate future research. WHAT THIS PAPER ADDS: There is inconsistency in use of the term severe neurological impairment (SNI), limiting research efforts. In defining SNI, considerations are mobility, intellectual disability, communication difficulties, and increased care needs. Distinction between acute and chronic, central and peripheral nervous system disorders, and dependence on others for decision-making were less significant.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".