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Record W4387624426 · doi:10.1016/j.hctj.2023.100023

Describing healthcare concerns of adolescents and adults with cerebral palsy

2023· article· en· W4387624426 on OpenAlexafffund
Christina M. Winger, Caitlin Cassidy, Jessica Starowicz, Laura Brunton

Bibliographic record

VenueHealth Care Transitions · 2023
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster UniversityWestern University
FundersAcademic Medical Organization of Southwestern Ontario
KeywordsCerebral palsyHealth careMedicinePsychologyPhysical medicine and rehabilitationPolitical science

Abstract

fetched live from OpenAlex

To identify healthcare concerns of adolescents and adults with cerebral palsy (CP) followed in a multidisciplinary rehabilitation program and identify patient factors associated with the number of concerns raised. A retrospective chart review of initial consultations of 241 people with CP (53 % male) aged 14 years or older (mean 27 y 5mo, SD 13 y 2mo), over a three-year period. Descriptive statistics were used to summarize data and explore associations. Poisson’s regression was used to predict healthcare concerns from patient demographic factors. A total of 2237 distinct concerns were raised by the participants, with a median of 9 (range 1–34) concerns per person. Ten healthcare concern categories were reported by more than 25 % of the sample. Only age was associated with the number of healthcare concerns (r = 0.25, p < 0.001). Age and GMFCS significantly predicted total number of healthcare concerns. Adolescents and adults with CP reported a high number of healthcare concerns at the initial visit to the Transitional and Lifelong Care program and the number of concerns may increase with advancing age. The concerns identified span a variety of biopsychosocial spheres and supports the need for ongoing specialty and multidisciplinary care of this population through their adult years.

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 score0.270
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.038
GPT teacher head0.306
Teacher spread0.268 · 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

Citations3
Published2023
Admission routes2
Has abstractyes

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