Reporting of methodological studies in health research: a protocol for the development of the MethodologIcal STudy reportIng Checklist (MISTIC)
Bibliographic record
Abstract
INTRODUCTION: Methodological studies (ie, studies that evaluate the design, conduct, analysis or reporting of other studies in health research) address various facets of health research including, for instance, data collection techniques, differences in approaches to analyses, reporting quality, adherence to guidelines or publication bias. As a result, methodological studies can help to identify knowledge gaps in the methodology of health research and strategies for improvement in research practices. Differences in methodological study names and a lack of reporting guidance contribute to lack of comparability across studies and difficulties in identifying relevant previous methodological studies. This paper outlines the methods we will use to develop an evidence-based tool-the MethodologIcal STudy reportIng Checklist-to harmonise naming conventions and improve the reporting of methodological studies. METHODS AND ANALYSIS: We will search for methodological studies in the Cumulative Index to Nursing and Allied Health Literature, Cochrane Library, Embase, MEDLINE, Web of Science, check reference lists and contact experts in the field. We will extract and summarise data on the study names, design and reporting features of the included methodological studies. Consensus on study terms and recommended reporting items will be achieved via video conference meetings with a panel of experts including researchers who have published methodological studies. ETHICS AND DISSEMINATION: The consensus study has been exempt from ethics review by the Hamilton Integrated Research Ethics Board. The results of the review and the reporting guideline will be disseminated in stakeholder meetings, conferences, peer-reviewed publications, in requests to journal editors (to endorse or make the guideline a requirement for authors), and on the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) Network and reporting guideline websites. REGISTRATION: We have registered the development of the reporting guideline with the EQUATOR Network and publicly posted this project on the Open Science Framework (www.osf.io/9hgbq).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.888 | 0.933 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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; both teacher heads 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".