Cerebral Palsy Research Network Clinical Registry: Methodology and Baseline Report
Bibliographic record
Abstract
OBJECTIVE: To apply practice-based evidence to clinical management of cerebral palsy (CP). The process of establishing purpose, structure, logistics, and elements of a multi-institutional registry and the baseline characteristics of initial enrollees are reported. DESIGN: A consensus-building process among consumers, clinicians, and researchers used a participatory action process. SETTING: Community, hospitals, and universities. PARTICIPANTS: More than 100 clinicians, researchers, and consumers and more than 1858 enrollees in the registry. MAIN OUTCOME MEASURES: Not applicable. RESULTS: Consensus was that the purpose of registry was to (1) quantify practice variation, (2) facilitate quality improvement (QI), and (3) perform comparative effectiveness research (CER). Collecting data during routine clinical care using the electronic medical record was determined to be a sustainable plan for data acquisition and management. Clinicians from multiple disciplines defined salient characteristics of individuals and interventions for the registry elements. The registry was central to the clinical research network, and a leadership structure was created. A leading electronic health record platform adopted the registry elements. Twenty-four sites have initiated the data collection process and agreed to export data to the registry. Currently 12 are collecting data. Number of enrollees and characteristics were similar to other population registers. CONCLUSIONS: This is the first multi-institutional CP registry that contains the patient and treatment characteristics needed for QI and CER. The Cerebral Palsy Research Network registry elements are implemented in a versatile electronic platform and minimize burden to clinicians. The resultant registry is available for any institution to participate and is growing rapidly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.080 | 0.106 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.019 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".