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Record W2891281508 · doi:10.1093/ptj/pzy081

A Collaborative Approach to Decision Making Through Developmental Monitoring to Provide Individualized Services for Children With Cerebral Palsy

2018· article· en· W2891281508 on OpenAlexafffund
Doreen J. Bartlett, Sarah Westcott McCoy, Lisa A. Chiarello, Lisa Avery, Barbara Galuppi

Bibliographic record

VenuePhysical Therapy · 2018
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster UniversityWestern University
FundersCanadian Institutes of Health Research
KeywordsCerebral palsyMedicinePhysical medicine and rehabilitationPsychology

Abstract

fetched live from OpenAlex

In this Perspective, we suggest a process to improve physical and occupational therapists’ and families’ collaboration to provide appropriate, efficient, and effective evidence-based services to improve motor function, self-care performance, and participation in family and recreation activities for children with cerebral palsy (CP). This process is informed by 2 multisite prospective cohort studies (Move & PLAY and On Track). The heterogeneity of children with CP is described, limiting the utility of evidence from randomized controlled trials and systematic reviews to inform service planning for children with CP. An evidence-based alternative using prospective cohort studies that produce knowledge of determinants of outcomes important to children and families and methods for developmental monitoring using longitudinal developmental and reference percentile curves to inform individualized care is suggested. Guiding questions are provided to explore how knowledge of determinants and developmental monitoring can inform family-centered, collaborative, strengths-based, and focused service programs to support early development and function. Although this perspective paper is focused on children with CP, the research approach described for collection of useful information and the clinical method of data use may be helpful for people with other heterogeneous chronic health conditions in which physical and occupational therapists face similar challenges.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.500
Threshold uncertainty score0.703

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 head0.027
GPT teacher head0.332
Teacher spread0.305 · 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

Citations18
Published2018
Admission routes2
Has abstractyes

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