Research in nutritional supplements and nutraceuticals for health, physical activity, and performance: moving forward
Bibliographic record
Abstract
This Horizons is part of a series that identifies key, forward-thinking research questions and challenges that need to be addressed. Specifically, this Horizons paper discusses research in nutritional supplements and nutraceuticals for health, physical activity, and performance, and is the product of a discussion by an expert panel that took place in January 2018 prior to the Canadian Nutrition Society Thematic Conference “Advances in Sport Nutrition from Daily Living to High Performance Sport”. The objective of this Horizons paper was to identify core considerations for future studies for this research area, and how scientists can be leaders in the field to ensure the best quality science is available for decision makers. It is strongly recommended that the various elements highlighted throughout this Horizons paper will increase the awareness of the significant before-, during-, and after-research due-diligence required to produce research of the highest quality. While it is recognized that many scientists will not be able to meet all of these aspects, it is nonetheless important to consider the points outlined and to recognize that those elements that are not met in studies may be significant limitations. Highlights Research questions that are hypothesis-driven are the strongest, and when combined with careful planning of the study, the result will often be of the best quality. Studies with a strong experimental design help discern between evidence-based findings and those that have not been substantiated.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".