Nutrition Users’ Guides: RCTs Part 2 – structured guide for interpreting and applying study results from randomised controlled trials on therapy or prevention questions
Bibliographic record
Abstract
This article continues from a prior commentary on evaluating the risk of bias in randomised controlled trials addressing nutritional interventions. Having provided a synopsis of the risk of bias issues, we now address how to understand trial results, including the interpretation of best estimates of effect and the corresponding precision (eg, 95% CIs), as well as the applicability of the evidence to patients based on their unique circumstances (eg, patients' values and preferences when trading off potential desirable and undesirable health outcomes and indicators (eg, cholesterol), and the potential burden and cost of an intervention). Authors can express the estimates of effect for health outcomes and indicators in relative terms (relative risks, relative risk reductions, OR or HRs)-measures that are generally consistent across populations-and absolute terms (risk differences)-measures that are more intuitive to clinicians and patients. CIs, the range in which the true effect plausibly lies, capture the precision of estimates. To apply results to patients, clinicians should consider the extent to which the study participants were similar to their patients, the extent to which the interventions evaluated in the study are applicable to their patients and if all patient-important outcomes of potential benefit and harm were reported. Subsequently, clinicians should consider the values and preferences of their patients with respect to the balance of the benefits, harms and burdens (and possibly the costs) when making decisions about dietary interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".