Preprint Pointers From a Long COVID Scoping Review: Considerations for Source Selection and Searching
Bibliographic record
Abstract
This paper describes the search approach for preprints for a post COVID-19 condition (i.e., long COVID) scoping review, including source selection, search strategy development, challenges, and insights throughout a project life cycle. With the growth of medical preprints since the COVID-19 pandemic, information professionals and researchers should be aware that preprints are possible sources of evidence and be prepared to manage them in evidence reviews for COVID-19 topics and beyond. Preprints are not peer-reviewed but can include important evidence about emerging topics. Because of the importance of preprints to the scoping review, a preprint search of Europe PubMed Central (PMC) was added. Europe PMC and similar aggregators combine multiple preprint servers and often have Boolean search, but sometimes limited search functionalities or few export options. Strategy translation encountered challenges such as varying and inconsistent terminology for post-COVID-19 condition, a complex search, and negotiating large numbers of preprints with resource constraints. Europe PMC identified additional preprints for inclusion due to additional preprint server coverage. It was helpful to limit the preprint search to the title and abstract fields, and to run an extra Internet search for publication of included study preprints. Challenges and potential solutions are summarized to support those conducting preprint searches for COVID-19 and other topics.
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 | 0.029 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".