Best Practices for Conducting Observational Research to Assess the Relation between Nutrition and Bone: An International Working Group Summary
Bibliographic record
Abstract
Diet is a modifiable factor that can affect bone strength and integrity, and the risk of fractures. Currently, a hierarchy of scientific evidence contributes to our understanding of the role of diet on bone health and fracture risk. The strength of evidence is generally based on the type of study conducted, the quality of the methodology employed, the rigor and integrity of the data collected and analysis plan, and the transparency and completeness of the results. Randomized controlled trials (RCTs) are considered to be the gold standard from a clinical research paradigm, but there is a dearth of high-quality diet-related intervention trials with bone as the primary outcome, forcing the use of observational research to inform research and clinical practices. However, for observational research to be of the most utility, standardization and optimization of the study design, accurate and reliable measurement of key variables, and appropriate data analysis and data reporting are paramount. Although there have been recommendations made in relation to RCTs in the field of nutrition, no clear rubric exists for best practices in conducting observational research with regard to nutrition and bone health. Therefore, the purpose of this paper is to describe the best practices and considerations for designing, conducting, analyzing, interpreting, and reporting observational research specifically for understanding the role of nutrition in bone health, amassed by a global panel of scientific experts with strengths in bone, nutrition epidemiology, physical activity, public health, clinical and translational trials, and observational study methods. The global panel of scientific experts represents the leadership and selected participants from the 10th annual International Symposium for the Nutritional Aspects of Osteoporosis. The topics selected and best practices presented reflect expert opinion and areas of scientific expertise of the authors rather than a systematic or comprehensive literature review or professional reporting guidelines.
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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 | 0.837 | 0.823 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.014 |
| Bibliometrics | 0.031 | 0.025 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.029 | 0.023 |
| Open science | 0.021 | 0.028 |
| Research integrity | 0.026 | 0.042 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".