The cerebral palsy research network: Building a learning health network for cerebral palsy
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to measure the growth of the Cerebral Palsy (CP) Research Network towards becoming a Learning Health Network in order to guide future development. METHODS: Thirteen CP Research Network leaders completed the Network Maturity Grid (NMG) which consists of six domains with eight to 10 components each. The six domains are Systems of Leadership, Governance and Management, Quality Improvement, Engagement and Community, Data and Analytics, and Research. Radar mapping was utilized to display mean scores on a 5-point ordinal scale (1 = not started to 5 = idealized state) across domains and for individual components within domains. Consensus was reached for top priorities for the next 3-5 years. RESULTS: Domain scores ranged from 2.4 in Quality Improvement to 3.2 in System of Leadership. The lowest scoring component was clinician clinical decision support and the highest was common purpose. The following priority areas of focus were agreed upon moving forward: development of leaders, financial sustainability, quality improvement education and training, patient reported data, data quality and validation, and primary data collection. CONCLUSION: Results from this project will be utilized for strategic planning to improve the network. Conducting regular self-assessments of the network with the NMG will be useful in achieving the network's ultimate goal to improve care and outcomes for individuals with CP.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".