Nutrition users’ guides: systematic reviews part 1 –structured guide for methodological assessment, interpretation and application of systematic reviews and meta-analyses of non-randomised nutritional epidemiology studies
Bibliographic record
Abstract
Due to the challenges of conducting randomised controlled trials (randomised trials) of dietary interventions, evidence in nutrition often comes from non-randomised (observational) studies of nutritional exposures-called nutritional epidemiology studies. When using systematic reviews of such studies to advise patients or populations on optimal dietary habits, users of the evidence (eg, healthcare professionals such as clinicians, health service and policy workers) should first evaluate the rigour (validity) and utility (applicability) of the systematic review. Issues in making this judgement include whether the review addressed a sensible question; included an exhaustive literature search; was scrupulous in the selection of studies and the collection of data; and presented results in a useful manner. For sufficiently rigorous and useful reviews, evidence users must subsequently evaluate the certainty of the findings, which depends on assessments of risk of bias, inconsistency, imprecision, indirectness, effect size, dose-response and the likelihood of publication bias. Given the challenges of nutritional epidemiology, evidence users need to be diligent in assessing whether studies provide evidence of sufficient certainty to allow confident recommendations for patients regarding nutrition and dietary interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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; both teacher heads 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".