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Record W3137741505 · doi:10.1055/s-0041-1725991

The Benefits of Physical Activity in Children and Adolescents with Epilepsy: A Systematic Review

2021· review· en· W3137741505 on OpenAlexaboutno aff
Loyane de Fátima Svierkovski, Angélica Miki Stein, Timothy Gustavo Cavazzotto, Ana Carolina Paludo

Bibliographic record

VenueJournal of Pediatric Epilepsy · 2021
Typereview
Languageen
FieldMedicine
TopicEpilepsy research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsCINAHLEpilepsyMEDLINEIntervention (counseling)MedicinePsychological interventionPhysical therapyPhysical exerciseCognitionPsychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract The aim of this study was to review the literature about the effect of physical activity intervention in children and adolescents with epilepsy. Articles were searched in the central electronic databases of MEDLINE, Embase, PsycAriticles, and CINAHL for the following keywords: “epilepsy,” “seizure,” “physical activity,” “physical exercise,” “exercise therapy,” “sport,” using the Boolean operator “AND” and “OR.” The quality of the selected articles was evaluated by the Physiotherapy Evidence Database scale. Out of the 22 articles selected, 18 did not involve intervention or did not have pre- and postresults and therefore were excluded from the study. The remaining four were studies from Canada and Korea which comprised two long-period interventions and were included in the analysis. Both programs demonstrated a positive effect of physical activity on variables related to psychological well-being and cognitive function. All the four articles demonstrated a lower score of quality. In conclusion, reviewed studies suggest that physical exercise program induces some benefits in children and adolescents with epilepsy. However, the noncontrolled trials and the varied analyses (quantitative vs. qualitative) make it difficult to establish a consensus about benefits of physical activity in epilepsy.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.081
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.329
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 designSystematic review
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

Citations3
Published2021
Admission routes1
Has abstractyes

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