Evidence based healthcare. What are the limitations of its application and implementation in dentistry?
Bibliographic record
Abstract
Bioline International is an archived version of the "Bioline International" website that functioned as a not-for-proft scholarly publishing cooperative. This archived site contains web content, including text, images and URLs for internal links from the "http://www.bioline.org.br" domain The "Bioline International" website was a platform for hosting peer-reviewed and open access journals published in developing countries in fields such as public health, tropical medicine, international development, food and nutritional security, and biodiversity. The platform was conceived as a way of reducing the South to North kowledge gap, improving the visibility of essential research and knowledge from local and regional sources in developing countries across the world, and supporting the progress of science to develop a gobal-scale knowledge base in its fields of concern. Bioline International was launched in 1993 as a joint project between the Tropical Database (now, The Reference Center on Environmental Information) in Barzil and The Electronic Publishing Trust for Development in the United Kingdom. The CRIA provided server hosting, administration and development, which The EPT oversaw content management, project development and research. In 2000, The UTSC Library and UTSC's Department of Social Sciences took over the role previously occupied by The Electronic Publishing Trust for Development.
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 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 | 0.510 | 0.735 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.004 | 0.023 |
| Scholarly communication | 0.025 | 0.034 |
| Open science | 0.012 | 0.012 |
| Research integrity | 0.023 | 0.022 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".