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Record W4297742500 · doi:10.1080/j006v27n02_02

Horseback Riding as Therapy for Children with Cerebral Palsy

2007· article· en· W4297742500 on OpenAlexaffabout
Laurie Snider, Nicol Korner‐Bitensky, Catherine Kammann, Sarah E. Warner, Maysoun Saleh

Bibliographic record

VenuePhysical & Occupational Therapy In Pediatrics · 2007
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcGill University
Fundersnot available
KeywordsHorseback ridingCerebral palsyPhysical therapyMedicinePhysical medicine and rehabilitationContext (archaeology)Intervention (counseling)Randomized controlled trialPopulationTrunkRehabilitationMEDLINESurgeryNursing

Abstract

fetched live from OpenAlex

A systematic review of the literature on horseback riding therapy as an intervention for children with cerebral palsy (CP) was carried out. The terms horse, riding, hippotherapy, horseback riding therapy, equine movement therapy, and cerebral palsy were searched in electronic databases and hand searched. Retrieved articles were rated for methodological quality using PEDro scoring to assess the internal validity of randomized trials and the Newcastle Ottawa Quality Assessment Scale to assess cohort studies. PICO questioning (Population, Intervention, Comparison, and Outcomes) was used to identify questions of interest to clinicians for outcomes within the context of the International Classification of Functioning, Disability and Health. Levels of evidence were then accorded each PICO question. There is Level 2a evidence that hippotherapy is effective for treating muscle symmetry in the trunk and hip and that therapeutic horseback riding is effective for improved gross motor function when compared with regular therapy or time on a waiting list. No studies addressed participation outcomes.

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)0.006
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0070.007
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.030
GPT teacher head0.336
Teacher spread0.306 · 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

Citations31
Published2007
Admission routes2
Has abstractyes

Explore more

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