Control of Confounding and Reporting of Results in Causal Inference Studies. Guidance for Authors from Editors of Respiratory, Sleep, and Critical Care Journals
Bibliographic record
Abstract
The 21st century has brought with it a welcome call for increased rigor in observational research methods (1, 2). It is not that observational research methods are inherently flawed – they are not (3, 4). Observational studies can contribute valuable evidence supporting causal associations when designed and conducted using rigorous methods. The “flaws” are a result of reliance on outdated methodology, inadequate attention to threats to validity (such as confounding), opaque reporting of results, lack of replication, and a failure to interpret findings within the context of the limitations of observational research methodology. Aware of this situation and influenced by our experience as journal editors, we convened an ad hoc group of 47 editors of 35 respiratory, sleep, and critical care journals to offer guidance to authors, peer reviewers, and researchers on the design and reporting of observational causal inference studies. This guidance takes the form of a call for investigators to consider making major changes to their approach to such studies. This document represents our current best understanding of approaches to causal inference, an active area of research. We anticipate that best practice in this, as in any scientific endeavor, will continue to evolve, requiring this document to be updated every 5 to 10 years. We believe these changes will increase the rigor, validity, and value of the work we publish in our journals.
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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 | 0.719 | 0.926 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.004 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.014 | 0.012 |
| Research integrity | 0.020 | 0.029 |
| Insufficient payload (model declined to judge) | 0.012 | 0.010 |
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".