Methods for the Selection of Covariates in Nutritional Epidemiology Studies: A Meta-Epidemiological Review
Bibliographic record
Abstract
BACKGROUND: Observational studies provide important information about the effects of exposures that cannot be easily studied in clinical trials, such as nutritional exposures, but are subject to confounding. Investigators adjust for confounders by entering them as covariates in analytic models. OBJECTIVE: The aim of this study was to evaluate the reporting and credibility of methods for selection of covariates in nutritional epidemiology studies. METHODS: We sampled 150 nutritional epidemiology studies published in 2007/2008 and 2017/2018 from the top 5 high-impact nutrition and medical journals and extracted information on methods for selection of covariates. RESULTS: Most studies did not report selecting covariates a priori (94.0%) or criteria for selection of covariates (63.3%). There was general inconsistency in choice of covariates, even among studies investigating similar questions. One-third of studies did not acknowledge potential for residual confounding in their discussion. CONCLUSION: Studies often do not report methods for selection of covariates, follow available guidance for selection of covariates, nor discuss potential for residual confounding.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Methods · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Systematic review | low |
| gpt | Meta-epidemiology (broad) Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Other design | medium |
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 | 0.094 | 0.221 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.020 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".