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Record W7053206307

Uso de la Morinda Citrifolia (Noni) y Moringa Oleífera en Vinoterapia para pacientes con osteoartritis

2019· article· en· W7053206307 on OpenAlexaboutno aff

Bibliographic record

VenueDialnet (Universidad de la Rioja) · 2019
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsMorindaOsteoarthritisJoint diseaseQuality of life (healthcare)Rheumatoid arthritisLife qualityClinical trial
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Osteoarthritis is a chronic, degenerative disease characterized by the wear of articular \ncartilage whose clinical manifestations are pain, decreased joint mobility and muscle strength, rigidity, as \nwell as joint effusion in advanced stages of the disease. \nObjective: To demonstrate the beneficial effects of medicinal plant wine on the quality of life of patients \nwith osteoarthritis. \nMethods: An experimental study was conducted in parallel and double blind, in 400 patients who formed \ntwo groups of 200 each, who attended the orthopedics clinic, the Machaco Ameijeiras and Julio Antonio \nMella polyclinics, for presenting degenerative joint disease, in the period 2013-2016. The two groups were \nevaluated simultaneously. The product that was proposed for the study was a medicinal plant wine that \nincludes Morinda Citrifolia (Noni) and Moringa Oleífera. Quality of life related to the response to treatment \nwas evaluated by using an adaptation of the Western Ontario and Mc Master Universities questionnaire to \nassess pain, stiffness and functional capacity of patients before and after treatment. \nResults: The therapeutic scheme used exceeded 50% improvement of the areas evaluated (WOMAC). \nConclusions: Pain, rigidity and functional capacity were improved in the patients after the treatment was \napplied.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.384
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.005
GPT teacher head0.216
Teacher spread0.212 · 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.

Study designBench or experimental
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

Citations0
Published2019
Admission routes1
Has abstractyes

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