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Record W4394844297 · doi:10.1016/j.gpeds.2024.100172

Quality of life in a cohort of Kenyan children with cerebral palsy

2024· article· en· W4394844297 on OpenAlexafffund
Pauline Samia, Melissa Tirkha, Amina-Inaara Kassam, Richard Muindi, Wahu Gitaka, Susan Wamithi, James Orwa, Eugene Were, Michael Shevell

Bibliographic record

VenueGlobal Pediatrics · 2024
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcGill UniversityWestern UniversityUniversity of Alberta
FundersFondation Rideau Hall
KeywordsRespite careCerebral palsyMedicineKenyaQuality of life (healthcare)Likert scalePopulationProxy (statistics)CohortPediatricsFamily medicinePsychologyPhysical therapyNursingEnvironmental healthDevelopmental psychology

Abstract

fetched live from OpenAlex

The objective of the study was to evaluate the quality of life in Kenyan children (age 4-18 years) with cerebral palsy (CP). A cross-sectional descriptive study was conducted. Children with CP were recruited from the paediatric clinics at the Aga Khan hospital Nairobi (AKUHN). Parent proxy-reports using CPQoL-child and CPQoL-adolescents were obtained. Clinical and demographic data were compiled from medical records and parent interviews. A Likert scale was utilized to determine QoL across several domains. One hundred and fourteen child–parent dyads with CP were recruited. The median age of study participants was 8 years (IQR 3-13 years), with males being the majority (57.02%). Parent proxy-reports using CPQoL-child scale were obtained for n=93 and CPQoL-adolescents for n=21 respondents. Parents in both groups reported low domain QoL scores pertaining to function, family health and rehabilitation service accessibility. Stigma, accessibility to services, therapies and schooling, particularly for children with severe functional limitations, remains a concern. Caregivers would benefit from awareness campaigns of available supports and from local community respite programs. Where national support systems exist, there are critical inefficiencies in service delivery to target population.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.014
GPT teacher head0.285
Teacher spread0.271 · 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.

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

Citations2
Published2024
Admission routes2
Has abstractyes

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