The currency and completeness of specialized databases of COVID-19 publications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Several specialized collections of COVID-19 literature have been developed during the global health emergency. These include the WHO COVID-19 Global Literature Database, Cochrane COVID-19 Study Register, CAMARADES COVID-19 SOLES, Epistemonikos' COVID-19 L-OVE, and LitCovid. Our objective was to evaluate the completeness of these collections and to measure the time from when COVID-19 articles are posted to when they appear in the collections. STUDY DESIGN AND SETTING: We tested each selected collection for the presence of 440 included studies from 25 COVID-19 systematic reviews. We sampled 112 journals and prospectively monitored their websites until a new COVID-19 article appeared. We then monitored for 2 weeks to see when the new articles appeared in each collection. PubMed served as a comparator. RESULTS: Every collection provided at least one record not found in PubMed. Four records (1%) were not in any of the sources studied. Collections contained between 83% and 93% of the primary studies with the WHO database being the most complete. By 2 weeks, between 60% and 78% of tracked articles had appeared. CONCLUSION: Our findings support the use of the best performing COVID-19 collections by systematic reviews to replace paywalled databases.
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.
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 | 0.181 | 0.707 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it