MétaCan
Menu
Back to cohort
Record W3016646572 · doi:10.1111/dmcn.14536

Healthcare use by children and young adults with cerebral palsy

2020· article· en· W3016646572 on OpenAlexfundno aff
Bethan Carter, Verity Bennett, Hywel Jones, Jackie Bethel, Oliver Perra, Ting Wang, Alison Kemp

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2020
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
FundersQueen's UniversityPublic Health AgencySwansea UniversityScottish Government
KeywordsCerebral palsyHealth careMedicinePsychologyPediatricsPhysical medicine and rehabilitationPolitical science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.225
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueDevelopmental Medicine & Child NeurologySame topicCerebral Palsy and Movement DisordersFrench-language works237,207