Risk of Bias in Randomized Controlled Trials of Nutrition Interventions for Frailty in Older Adults
Bibliographic record
Abstract
Abstract We assessed the risk of bias in randomized controlled trials (RCTs) of nutrition-only interventions and holistic frailty outcomes in older adults. We also explored associations between study-level factors and risk of bias. We searched Cochrane, PubMed, and Scopus for published trials between 01/01/2000 and 11/13/2024. Two persons independently screened each citation at the title and abstract, and full text, levels. They also independently conducted data extraction and assessed risk of bias using the Cochrane Risk of Bias 2 tool. We used responses on the tool to develop index scores between 0-1 for each included article, with higher scores indicating lower risk of bias. We regressed the index scores on four study-level factors, i.e., region of publication, year of publication, journal impact factor, and reported use of CONSORT guidelines. Fifteen articles were included in the study: three had low risk of bias, two had some concerns with bias, and ten had high risk of bias. Domain 2 on the Cochrane tool generated the most challenges with bias, largely due to poor reporting of intention-to-treat analysis and lack of information on how this issue might affect trial results. Median index scores were 0.52, 0.53, and 0.86 for articles with high, some concerns, and low risk of bias, respectively (p = 0.0479). However, the index scores were not associated with any study-level factors. Researchers in the field should note potential biases in the design and conduct of RCTs, especially in data analysis and – more specifically – intent-to-treat analysis.
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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 | 0.599 | 0.826 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.019 | 0.033 |
| Bibliometrics | 0.026 | 0.021 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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, 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".