Bibliographic record
Abstract
Cerebral palsy is a heterogeneous syndrome that is the most common form of physical impairment encountered in pediatrics. Its heterogeneity, which is apparent in all aspects of the disorder, challenges our attempts to classify it. Several classification structures do exist that seek to further our understanding of the basic mechanisms and needs associated with this entity. The most long-standing classification approach utilizes the neurologic examination to characterize and stratify the predominant qualitative pattern of motor impairment (i.e., spastic, dyskinetic, ataxic–hypotonic or mixed), and if spastic, the particular limb distribution. The severity of cerebral palsy can be summarized in the domains of gross motor and fine motor skills by the Gross Motor Function Classification System and the Manual Ability Classification System, respectively. Frequently for patients with cerebral palsy, the major health burden may not be that of a neuromotor impairment, but rather that of the associated conditions (i.e., epilepsy, intellectual disability, etc.) affecting the individual. Finally, one may employ a mechanistic approach to stratifying according to imaging results and etiology, which are linked and provide an insight into the pathogenesis and the timing of malformation or acquired injury. While the approaches used in each of these classification schemes are separate, distinct and single axial, inter-relationships are readily apparent. Each of the classification approaches capture only one aspect of a complex disorder and is thus too simplistic. A multimodal classification approach can be employed in a complimentary fashion to provide a more holistic profile of the individual with cerebral palsy.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".