The EQUATOR Network – a New Web Resource for Good Reporting of Health Research
Bibliographic record
Abstract
Too often, good research evidence is undermined by the poor quality of scientific reporting. The EQUATOR Network http://www.equator-network.org is a new initiative that seeks to improve the quality and reliability of health research literature. EQUATOR Network has launched a new website that provides unique access to collated expertise and resources for good reporting of health research. The resources are aimed at researchers (authors of research articles), journal editors, peer reviewers, and developers of reporting guidelines. The most important website feature is a collection of reporting guidelines (see http://www.equator-network.org/index.aspx?o=1032). The last decade saw an expansion in the development of guidelines that specify minimum criteria for reporting various study designs. Of these guidelines, CONSORT, for reporting randomised controlled trials (see http://www.consort-statement.org) and QUOROM, for systematic reviews (see http://www.consort-statement.org/mod_product/uploads/QUORUM Statement 1999.pdf) are probably the best known. The EQUATOR site brings all available resources under ‘one roof’ and its team is extensively networking and searching to keep the collection up-to-date. To become a truly useful resource for its users, the EQUATOR Network plans further development and a set of key deliverables, including: a Web-based Resource Centre with easy access to reporting guidelines and other information; training courses for authors, editors and peer reviewers, to facilitate transparent and accurate reporting and use of reporting guidelines; support to the developers of reporting guidelines; annual assessment of how journals implement reporting guidelines; annual audit of reporting quality across the health research literature. Professor Doug Altman, one of the key movers of the CONSORT initiative, is leading the EQUATOR project. The EQUATOR Network international steering group includes leading experts in the fields of health research methodology, reporting and editorial work. Funding The UK NHS National Knowledge Service provided the initial funding for the project. However, in order to have any meaningful impact on the quality of reporting of research publications we need to secure more funds from different organisations. Funding opportunities for various EQUATOR activities are outlined on our website. For more information please visit the EQUATOR Network website at http://www.equator-network.org/ Contact: Iveta Simera by e-mail: iveta.simera@cancer.org.uk Please feel free to link to the EQUATOR site from your website to facilitate use of our resources.
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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 | 0.087 | 0.319 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.019 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.014 | 0.017 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.201 | 0.187 |
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".