Perspectives on classification of selected childhood neurodisabilities based on a review of literature
Bibliographic record
Abstract
PURPOSE: Classifying children with heterogeneous health conditions is challenging. The purposes of this perspective are to explore the prevailing classifications in children with the three selected neurodisabilities using the underlying framework of ICF/ICF-CY, explore the utility of the identified classifications, and make recommendations aimed at improving classifications. METHODS: A literature search on six databases and Google was conducted. Articles published between the years 2000 and 2013 were included if they provided information on classification of cerebral palsy (CP), and/or developmental coordination disorder (DCD) and/or autism spectrum disorders (ASD). RESULTS: Children with DCD and ASD are classified using combinations of multiple measures. The classifications in CP meet more of the proposed criteria for utility than those in DCD and ASD. CONCLUSION: None of the existing classifications addressed all the criteria. The heterogeneity associated with the selected neurodisabilities poses major challenges. Further work is required to establish improved classifications.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.030 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".