Reporting guidelines for health research: protocol for a cross-sectional analysis of the EQUATOR Network Library
Bibliographic record
Abstract
INTRODUCTION: Transparency and completeness of health research is highly variable, with important deficiencies in the reporting of methods and results of studies. Reporting guidelines aim to improve transparency and quality of research reports, and are often developed by consortia of journal editors, peer reviewers, authors, consumers and other key stakeholders. The objective of this study will be to investigate the characteristics of scientific collaboration among developers and the citation metrics of reporting guidelines of health research. METHODS AND ANALYSIS: This is the study protocol for a cross-sectional analysis of completed reporting guidelines indexed in the Enhancing the QUAlity and Transparency Of health Research Network Library. We will search PubMed/MEDLINE and the Web of Science. Screening, selection and data abstraction will be conducted by one researcher and verified by a second researcher. Potential discrepancies will be resolved via discussion. We will include published papers of reporting guidelines written in English. Published papers will have to meet the definition of a reporting guideline related to health research (eg, a checklist, flow diagram or explicit text), with no restrictions by study design, medical specialty, disease or condition. Raw data from each included paper (including title, publication year, journal, subject category, keywords, citations, and the authors' names, author's affiliated institution and country) will be exported from the Web of Science. Descriptive analyses will be conducted (including the number of papers, citations, authors, countries, journals, keywords and main collaboration metrics). We will identify the most prolific authors, institutions, countries, journals and the most cited papers. Network analyses will be carried out to study the structure of collaborations. ETHICS AND DISSEMINATION: No ethical approval will be required. Findings from this study will be published in peer-reviewed journals. All data will be deposited in a cross-disciplinary public repository. It is anticipated the study findings could be relevant to a variety of audiences.
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 | 0.353 | 0.385 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.078 | 0.032 |
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".