Availability of renal literature in six bibliographic databases
Bibliographic record
Abstract
BACKGROUND: When searching for renal literature, nephrologists must choose between several different bibliographic databases. We compared the availability of renal clinical studies in six major bibliographic databases. METHODS: We gathered 151 renal systematic reviews, which collectively contained 2195 unique citations referencing primary studies in the form of journal articles, meeting articles or meeting abstracts published between 1963 and 2008. We searched for each citation in three subscription-free bibliographic databases (PubMed, Google Scholar and Scirus) and three subscription-based databases (EMBASE, Ovid-MEDLINE and ISI Web of Knowledge). For the subscription-free databases, we determined which full-text journal articles were available free of charge via links to the article source. RESULTS: The proportion of journal articles contained within each of the six databases ranged from 96 to 97%; results were similar for meeting articles. Availability of meeting abstracts was poor, ranging from 0 to 37% (P < 0.01) with ISI Web of Knowledge containing the largest proportion [37%, 95% confidence interval (95% CI) 32-43%]. Among the subscription-free databases, free access to full-text articles was highest in Google Scholar (38% free, 95% CI 36-41%), and was only marginally higher (39%) when all subscription-free databases were searched. After 2000, free access to full-text articles increased to 49%. CONCLUSIONS: Over 99% of renal clinical journal articles are available in at least one major bibliographic database. Subscription-free databases provide free full-text access to almost half of the articles published after the year 2000, which may be of particular interest to clinicians in settings with limited access to subscription-based resources.
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 | 0.332 | 0.328 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".