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Record W2033112960 · doi:10.1177/0883073814533423

The Concept of a Toolbox of Outcome Measures for Children With Cerebral Palsy

2014· review· en· W2033112960 on OpenAlexafffund
F. Virginia Wright, Annette Majnemer

Bibliographic record

VenueJournal of Child Neurology · 2014
Typereview
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcGill UniversityMcGill University Health CentreHolland Bloorview Kids Rehabilitation HospitalMontreal Children's HospitalUniversity of Toronto
FundersHolland Bloorview Kids Rehabilitation Hospital Foundation
KeywordsToolboxCerebral palsyInternational Classification of Functioning, Disability and HealthIntervention (counseling)PsychologyOutcome (game theory)RehabilitationIdentification (biology)Process (computing)Plan (archaeology)Developmental psychologyApplied psychologyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

Accurate and well-targeted measurement of a child's abilities and participation in daily activities pre- and post-intervention is essential to understanding the effects of therapies provided by pediatric practitioners. There is growing interest in identification of outcome core sets for specified client groups. This article elaborates on the concepts to consider when selecting and interpreting measures from an outcomes toolbox for children with cerebral palsy. Principles discussed include use of self-report measures to open a dialogue with the child/parent; a holistic assessment approach to identify a child's challenges, strengths, and contextual factors that can influence functioning; links between measurement and heightened engagement of the child/family in the rehabilitation process and goals; and the need to plan the evaluation and dialogue aspects of the assessment process. If clinicians across the international rehabilitation community draw from the same toolbox, the end result could be a cohesive approach and common language to outcome measurement.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score0.651

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
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 head0.032
GPT teacher head0.316
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations45
Published2014
Admission routes2
Has abstractyes

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