G45(P) Severe neurological impairment: towards an international consensus-based definition
Bibliographic record
Abstract
Aims Following a thorough review of the literature, we have found significant inconsistency in the use of the term Severe Neurological Impairment (SNI) in the literature. We aimed to develop an international, consensus-based, multi-disciplinary definition of this term. Methods The Delphi process was chosen to achieve consensus on the definition of SNI. We collaborated with experts in 5 countries (Ireland, the UK, the USA, Canada and Australia) to disseminate an invitation to other colleagues in neurodisability in their own region. We specified that a multi-disciplinary panel was required. Those who wished to participate were asked to email us, as facilitators of the process, to confirm their desire to take part. Participants were asked to further disseminate the invitation to other colleagues, thus employing a snow-balling effect in the recruitment of expert panellists. The Delphi process proceeded over 3 rounds. Round 1 used free-text responses where panellists provided insight into their understanding of the term SNI. Responses were used to generate themes. In rounds 2 and 3 panellists were asked to rate their agreement with these themes in the definition of SNI. In the process of round 3 participants were provided with feedback on the previous round, including anonymous information on how the other panellists had voted as well as selected written feedback to provide an opportunity to consider other points of view. Items were brought forward to the final definition if they received more than 70% agreement, in line with accepted Delphi methodology. After round 3, a working definition of SNI was created. Further refinements were made based on comments from parent representatives and experts at an international conference. Results Thirty-four multi-disciplinary panellists participated in round 1 of the process falling to 31 in round 3, a 9% drop-out rate. Fifteen themes were generated from responses in round 1. Seven items were brought forward for inclusion in the final definition. Conclusion We have created an international, multi-disciplinary, consensus-based definition of SNI. This definition can be used to improve consistency in reporting of research, ultimately leading to improved outcomes for this unique and vulnerable cohort of children.
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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 | 0.108 | 0.154 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".