PRISMA-DTA for Abstracts: a new addition to the toolbox for test accuracy research
Bibliographic record
Abstract
Reporting guidelines for abstractsEarlier versions of reporting guidelines primarily provided guidance for full-text articles, but there has also been growing attention for reporting journal and conference abstracts over the years. This started with CONSORT for Abstracts, which was published in 2008 as an extension of CONSORT and provided guidance for reporting abstracts of clinical trials [14]. Since then, extensions of other reporting guidelines specifically focusing on the reporting of abstracts have been developed. Currently reporting guidelines for abstracts are available for at least five types of study designs: clinical trials, observational studies, systematic reviews, test accuracy studies, overviews of systematic reviews, and multivariable prediction models [15, 16]. More are likely to follow.The abstract has become a fundamental part of a study report, which may have a considerable impact on the interpretation of a study for the average reader. Many users of the biomedical literature only read the abstract, either due to time constraints or because they do not have access to the full text. In addition, systematic reviewers and guideline developers rely on accurate information in the abstract because they often need to screen large amounts of them for potential eligibility. Also, if a study is presented at a scientific conference, the abstract is often the only bit of information available about the study, and many studies reported as conference abstracts are never published in full [17].It has been shown numerous times that reporting in abstracts, also in test accuracy research, is frequently incomplete, which could lead to misinterpretation and overinterpretation of the study findings [18,19,20]. This may be the case if crucial design elements resulting in potential sources of bias or generalizability concerns are not evident, or if the authors "spin" their findings, which has shown to be more frequent in abstracts than in full texts [21,22,23].
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 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 | 0.200 | 0.946 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".