Secondary care provision for children and young people with Cerebral palsy: A data-linkage study
Bibliographic record
Abstract
IntroductionData on children with cerebral palsies are often held in registries, but these contain limited information with varying levels of follow-up. Here we show how record-linkage with healthcare datasets has enabled longitudinal follow-up if these children to understand how they use secondary care health services. Objectives and ApproachOur primary aim was to explore healthcare utilisation for children and young people (CYP) with CP aged 0-25 years between 2004 and 2014 by severity, measured by recorded Gross Motor Function Classification System (GMFCS) level. This was achieved by linking Northern Ireland Cerebral Palsy Register (NICPR) data to routinely collected secondary care data. Comparison was made to the population of CYP who were not on the NICPR i.e. non CP cases. ResultsThere were 1,693 cases in the NICPR cohort born 1981-2011. Of those, 286 (16.9%) were GMFCS 1, 662 (39.1%) GMFCS 2, 277 (16.4%) GMFCS 3, 105 (6.2%) GMFCS 4 and 342 (20.2%) were GMFCS 5 (21 (1.2%) missing). NICPR cases had 11,844 hospital admissions and 19,750 outpatient appointments during the study period accounting for 1.7% of both inpatient and outpatient attendances. Those with severe CP were more likely to have an inpatient admission and had longer stays in hospital than those with less severe CP and those without CP. 592/948 (62.4%) patients with GMFCS 1&2 had an admission compared to 345/447 (77.2%) of GMFCS 4&5 cases. The proportion of elective to emergency admissions was 72.4% versus 53.7% for non CP. Conclusion/ImplicationsThis study adds to understanding of service utilisation for those with CP in the UK, and provides comparable figures with a recent study in Australia. Thus, further demonstrating that linkage between CP registers and routinely collected healthcare may be useful for health services research and informing healthcare delivery.
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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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".