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Record W2792844514 · doi:10.1097/pep.0000000000000490

Descriptive Report of the Impact of Fatigue and Current Management Strategies in Cerebral Palsy

2018· article· en· W2792844514 on OpenAlexafffund
Laura Brunton

Bibliographic record

VenuePediatric Physical Therapy · 2018
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsCanadian Institutes of Health ResearchWestern University
FundersCanadian Institutes of Health Research
KeywordsCerebral palsyGross Motor Function Classification SystemPhysical therapyPhysical medicine and rehabilitationMedicinePsychology

Abstract

fetched live from OpenAlex

PURPOSE: To describe the effect of fatigue and self-management practices for adolescents and young adults with cerebral palsy. METHODS: A survey of 124 people with cerebral palsy with the Fatigue Impact and Severity Self-Assessment. RESULTS: Participants in Gross Motor Function Classification System (GMFCS) level I experienced little effect of fatigue, with high proportions of this group disagreeing to statements about fatigue impacting their general activities, mobility, and social activities. Participants in GMFCS levels II to V reported effect of fatigue on activities. Differences between groups were evident in questions related to fatigue interference with length of time for physical activity and with motivations to participate in social activities. All other items related to management of fatigue were not significantly different between groups. CONCLUSIONS: Fatigue effect is greater for participants with more functional limitations. The lack of significant differences between groups, on the Management and Activity Modification subscale, indicates more research is needed regarding strategies for fatigue management.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.005
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.343
Teacher spread0.304 · 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

Citations17
Published2018
Admission routes2
Has abstractyes

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