Nutrition users’ guides: RCTs part 1 – structured guide for assessing risk of bias in randomised controlled trials that address therapy or prevention questions
Bibliographic record
Abstract
The purpose of this article, part 1 of 2 on randomised controlled trials (RCTs), is to provide readers (eg, clinicians, patients, health service and policy decision-makers) of the nutrition literature structured guidance on interpreting RCTs. Evaluation of a given RCT involves several considerations, including the potential for risk of bias, the assessment of estimates of effect and their corresponding precision, and the applicability of the evidence to one's patient. Risk of bias refers to flaws in the design or conduct of a study that may lead to a deviation from measuring the underlying true effect of an intervention. Bias is assessed on a continuum from very low to very high (ie, definitely low to definitely high) risk of yielding estimates that do not represent true treatment-related effects and when appraising a study, judgement involves some degree of subjectivity. Specifically, when evaluating the risk of bias, one must first consider whether patient baseline characteristics (eg, age, smoking) are balanced between groups at randomisation, referred to as prognostic balance, and whether this balance is maintained throughout the study. While randomisation in sufficiently large trials may ensure prognostic balance between study arms at baseline; concealment of randomisation and blinding of participants, healthcare providers, data collectors, outcome adjudicators and data analysts to treatment allocation are needed to maintain prognostic balance between study arms after a trial begins. The status of each participant with respect to outcomes of interest must be known at the conclusion of a trial; when this is not the case, missing (lost) participant outcome data increases the likelihood that prognostic balance was not maintained at study completion. In addition, analysis of participants in the groups to which they were initially randomised (ie, intention-to-treat analysis) offers a reliable method to maintain prognostic balance. Finally, trials terminated early risk overestimating the treatment effect, especially when sample size is limited or stopping boundaries are not defined a priori.
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 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 | 0.132 | 0.508 |
| Meta-epidemiology (narrow) | 0.005 | 0.007 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.009 | 0.007 |
| Research integrity | 0.019 | 0.015 |
| Insufficient payload (model declined to judge) | 0.189 | 0.128 |
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 source (direct Gemma or distilled Codex), 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".