Healthcare use by children and young adults with cerebral palsy
Bibliographic record
Abstract
AIM: To link routinely collected health data to a cerebral palsy (CP) register in order to enable analysis of healthcare use by severity of CP. METHOD: The Northern Ireland Cerebral Palsy Register was linked to hospital data. Data for those on the CP register born between 1st January 1981 and 31st December 2009 and alive in 2004 were extracted, forming a CP cohort (n=1684; 57% males, 43% females; aged 0-24y). Frequencies of healthcare events, and the reasons for them, were reported according to CP severity and compared with those without CP who had had at least one hospital attendance in Northern Ireland within the study period. RESULTS: Cases of CP represented 0.3% of the Northern Ireland population aged 0 to 24 years but accounted for 1.6% of hospital admissions and 1.6% of outpatient appointments. They had higher rates of elective admissions and multi-day hospital stays than the general population. Respiratory conditions were the most common reason for emergency admissions. Those with most severe CP were 10 times more likely to be admitted, and four times more likely to attend outpatients, than those with mild CP. INTERPRETATION: Linkage between a register and routinely collected healthcare data provided a confirmed cohort of cases of CP that was sufficiently detailed to analyse healthcare use by disease severity.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".