Developing a Web 2.0 Telemedical Education System: the AJAX-Cocoon Portal
Bibliographic record
Abstract
Web 2.0 technologies such as wikis, podcasts/vodcasting, blogs and semantic portals could be quite effective tools in e-learning for health professionals. If effectively deployed, such tools can offer a way to enhance students', clinicians' and patients' learning experiences, and deepens levels of learners' engagement and collaboration within medical learning environments. However, Web 2.0 requires simplicity of use as well as integration with modern web technologies. This article presents a Web 2.0 telemedical portal, which provides a social community-learning paradigm from the desk of the physician, the student, the hospital administrator, or the insurer. The presented portal utilises RESTful web services and techniques like content syndication, mushups and Asynchronous JavaScript API and XML (AJAX). The designed portal is based on the Apache Cocoon RESTful framework for sharing Digital Imaging and Communications in Medicine (DICOM) medical case studies. Central to this article is the integration between Cocoon and AJAX. The proposed AJAX-Cocoon portal utilises a JSP portlet architecture, which manages the interaction dynamics and overcomes the shortcomings of the JSR 168 and WSRP 1.0 standards.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".