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Record W2018240508 · doi:10.5585/conssaude.v13n1.4388

Quality of life in multiple sclerosis patients participating in therapeutic horseback riding

2014· article· en· W2018240508 on OpenAlexaff
Fabiana Moraes Flores, Luana Santos Sousa, Karla Mendonça Menezes, Fernando Copetti, Cláudia Morais Trevisan

Bibliographic record

VenueConScientiae Saúde · 2014
Typearticle
Languageen
FieldMedicine
TopicMultiple Sclerosis Research Studies
Canadian institutionsAdidas (Canada)
Fundersnot available
KeywordsHorseback ridingQuality of life (healthcare)Multiple sclerosisIntervention (counseling)Physical therapyMedicinePhysical medicine and rehabilitationPsychologyPsychiatryNursing

Abstract

fetched live from OpenAlex

Introduction: Multiple Sclerosis (MS) is a disease that causes interferences in the quality of life (QoL) of affected individuals; for this reason, for this reason, programs of activities that can improve quality of life are crucial. Objective: Investigate whether therapeutic horseback riding (THR) included in the routine activities of people with MS promotes changes in perceived QoL. Methods: Fourteen individuals with MS were divided into intervention group and control group and assessed for perceived QoL by means of the Functional Assessment of Multiple Sclerosis (FAMS) instrument, before and after an interval of four months. Results: No significant changes in the QoL of MS subjects were observed, either in intragroup or intergroup assessments. Conclusion: The intervention with THR included in the routine therapeutic activities of individuals with MS was not helpful to promote changes in the perceived QoL of the individuals studied.

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)0.002
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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.219
GPT teacher head0.367
Teacher spread0.148 · 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

Citations7
Published2014
Admission routes1
Has abstractyes

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