Uso de la Morinda Citrifolia (Noni) y Moringa Oleífera en Vinoterapia para pacientes con osteoartritis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".