Índice inflamatorio dietético relacionado con la sintomatología de osteoartritis de rodilla
Bibliographic record
Abstract
Resumen Introducción: la dieta proinflamatoria contribuye a una mayor sintomatología en pacientes con osteoartritis de rodilla (OAR); sin embargo, en México parece no existir evidencia del papel inflamatorio dietético, pues es un país con alta prevalencia de sobrepeso y obesidad con inclinación hacia una dieta occidental. Objetivo: analizar la relación del índice inflamatorio dietético (IID) con la sintomatología de OAR en pacientes mexicanos. Material y métodos: estudio transversal, analítico en 100 pacientes de 40 a 70 años. Se evaluó el dolor, la rigidez y la funcionalidad con el Western Ontario and McMaster Universities Arthritis Index (WOMAC) y el IID se calculó a partir del cuestionario semicuantitativo de frecuencia de consumo de alimentos (CSFC). Para su análisis, se calculó regresión lineal. Resultados: el IID se asoció significativamente con dolor (p = 0.001, R² = 0.118), funcionalidad (p = 0.003, R² = 0.087) y puntaje del WOMAC (p = 0.001, R² = 0.099). En el segundo modelo de regresión lineal con la variable dependiente funcionalidad, se ajustó la circunferencia de cintura (CC) y se obtuvo una R² = 0.144 y una mayor significación: p = 0.001. Conclusiones: el IID proinflamatorio se relacionó con un mayor dolor, una menor funcionalidad y un puntaje alto del WOMAC, por lo cual la dieta antiinflamatoria podría considerarse como un apoyo para el tratamiento del paciente con OAR.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".