STROBE-MR: Guidelines for strengthening the reporting of Mendelian randomization studies
Bibliographic record
Abstract
While the number of studies using Mendelian randomization (MR) methods has grown exponentially in the last decade, the quality of reporting of these studies often has been poor. Similar to other reporting guidelines such as CONSORT ( Consolidated Standards of Reporting Trials ) for randomised trials and STROBE ( STrenghtening the Reporting of Observational studies in Epidemiology ) for observational studies in epidemiology, the STROBE-MR working group aims to provide guidance to authors on how to improve reporting of MR studies and help readers, reviewers, and journal editors to evaluate the quality of the presented evidence. Empirical evidence indicates that many reports of MR studies do not clearly state or examine the various assumptions of MR methods and report insufficient details on the data sources, which makes it hard to evaluate the quality and reliability of the results. The STROBE-MR guidance covers both one sample and two sample MR studies. At present, the draft checklist consists of 20 items, organized into the title and abstract, introduction, methods, results and discussion sections of articles. As these guidelines aim to reach the entire MR community, we would like to give everyone the opportunity to contribute their comments. The following draft of the STROBE-MR checklist is open for public discussion and all feedback will be taken into account during its next revision. For feedback, please use the comment section below this post on PeerJ Preprints. We hope the final guidelines will serve the entire community and contribute to improving the reporting of MR studies in the future.
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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 | 0.508 | 0.773 |
| Meta-epidemiology (narrow) | 0.006 | 0.008 |
| Meta-epidemiology (broad) | 0.012 | 0.018 |
| Bibliometrics | 0.034 | 0.029 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.013 | 0.014 |
| Research integrity | 0.015 | 0.023 |
| Insufficient payload (model declined to judge) | 0.057 | 0.041 |
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".