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Record W3151687669 · doi:10.1111/dmcn.14854

Data linkage and pain medication in people with cerebral palsy: a cross‐sectional study

2021· article· en· W3151687669 on OpenAlexfundno aff
Elena Guiomar García Jalón, Aideen Maguire, Oliver Perra, Anna Gavin, Dermot O’Reilly, Allen Thurston

Bibliographic record

VenueDevelopmental Medicine & Child Neurology · 2021
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsnot available
FundersQueen's UniversityEconomic and Social Research CouncilPublic Health Agency
KeywordsOdds ratioMedicineCerebral palsyCross-sectional studyConfidence intervalLogistic regressionPopulationPhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

AIM: To explore data linkage and pain medication as a proxy for pain, to assess differences in pain medication between the cerebral palsy (CP) and the general populations, and to identify factors associated with pain medication in CP. METHOD: This cross-sectional study linked the Northern Ireland CP Register and two administrative health care databases for people resident in Northern Ireland born between 1981 and 2008. Pain medication as a proxy was validated by replicating analyses from the Study of Participation of Children with Cerebral Palsy Living in Europe (SPARCLE) studies. Logistic regression compared pain medication in the CP and general populations. Multi-level regression models assessed factors associated with pain medication in the CP cohort. RESULTS: The sample size was 701 075, of whom 1430 (0.2%) were people with CP. There were 358 969 males and 340 677 females in the general population, and 810 males and 620 females in the CP population, with an age range of 4 to 31 years in both groups. The validation exercise produced results similar to the SPARCLE studies. More people with CP received pain medication (61% vs 50.9%) and had twice the odds of being prescribed opioid analgesics (odds ratio [OR]=2.81, 95% confidence interval [CI] 2.32-3.40). Among those with CP, the odds of being prescribed pain medication were higher for: females (OR=1.34, 95% CI 1.06-1.70), younger age (OR=1.60, 95% CI 1.02-2.51), Gross Motor Function Classification System level V (OR=2.60, 95% CI 1.52-4.47), seizures (OR=2.55, 95% CI 1.68-3.87), and higher deprivation score (OR=2.06, 95% CI 1.41-3.24). INTERPRETATION: Pain medication is an effective proxy for pain. More people with CP were prescribed pain medication than the general population. Pain medication for people with CP is not only dependent on physiological and clinical characteristics, but also environmental factors. What this paper adds Data linkage using pain medication as a proxy for experiencing pain is a valid method. People with cerebral palsy (CP) are more likely to experience pain than the general population. People with CP have over twice the odds of receiving opioids compared to the general population. The odds of being prescribed pain medication were higher for females with CP. Prescription of pain medication among those with CP is not only dependent on clinical characteristics, but also environmental factors.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.025
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.287
Teacher spread0.262 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations6
Published2021
Admission routes1
Has abstractyes

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