Combining pain therapy with lifestyle: the role of personalized nutrition and nutritional supplements according to the SIMPAR Feed Your Destiny approach
Bibliographic record
Abstract
Recently, attention to the lifestyle of patients has been rapidly increasing in the field of pain therapy, particularly with regard to the role of nutrition in pain development and its management. In this review, we summarize the latest findings on the role of nutrition and nutraceuticals, microbiome, obesity, soy, omega-3 fatty acids, and curcumin supplementation as key elements in modulating the efficacy of analgesic treatments, including opioids. These main topics were addressed during the first edition of the Study In Multidisciplinary Pain Research workshop: "FYD (Feed Your Destiny): Fighting Pain", held on April 7, 2016, in Rome, Italy, which was sponsored by a grant from the Italian Ministry of Instruction on "Nutraceuticals and Innovative Pharmacology". The take-home message of this workshop was the recognition that patients with chronic pain should undergo nutritional assessment and counseling, which should be initiated at the onset of treatment. Some foods and supplements used in personalized treatment will likely improve clinical outcomes of analgesic therapy and result in considerable improvement of patient compliance and quality of life. From our current perspective, the potential benefit of including nutrition in personalizing pain medicine is formidable and highly promising.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".