The Association Between Dietary Energy Density and Musculoskeletal Pain in Adult Men and Women
Bibliographic record
Abstract
Musculoskeletal pains (MPs), defined as persistent or recurrent pain, is a complex health problem. High overall calorie and fat intake have been related to obesity and MPs. Dietary energy density (DED), defined as energy content of food and beverages (in kcal) per unit total weight, has been associated with chronic muscle, cartilage, bone damage and pain. Thus, the purpose of this study is to investigate the association between DED and MPs in adult men and women. A total of 175 men and women (> 18 years) with MP participated in the study. A validated short form physical activity (PA) questionnaire, demographic, and McGill Pain Questionnaire were used. Anthropometric measurements were evaluated via standard protocols. Furthermore, a seven-day 24-hour recall of diet was used to determine the dietary intake. Total DED was calculated and divided into quartiles. Linear regression was used to discern the association between DED and MPs in adults. Participants assigned in the highest category of DED were characterized by lower intake of potassium, magnesium, vitamin C, folate, and fiber. However, results showed displayed higher intake of sodium, vitamin E, vitamin B3, fat, protein, cholesterol, saturated fatty acids, monounsaturated fatty acids, and polyunsaturated fatty acids (p < 0.001). Finally, after adjustment for confounders such as age, gender, PA, body mass index, waist circumference, education, job, marital status, history of some chronic diseases and vitamin C supplementation, a significant positive association was detected between DED and pain intensity. There was no significant association between DED and pain frequency in all models.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".