Medical Portals: Web-based access to medical information
Bibliographic record
Abstract
Public portals, such as Web search engines, have been available for a number of years and corporate portals that facilitate access to enterprise information within a company, normally through the Web, have been available for the last few years. Such portals are made up of "channels" of information and the purpose of these portals is to provide an interface that presents an organized view of the data to which the user has access, i.e., a straightforward means of access to this data. Both public and corporate portals provide access to potentially vast amounts of complex, distributed information through a Web browser. The infrastructures are based on Web technologies and the common interface is the Web browser. Medical information is vast, complex and distributed. Similar to corporate and public portals, medical portals can provide the medical community with access to medical information through the Web browser. Appropriate portals and channels within those portals can be defined to provide access from the desk of the physician, the hospital administrator, the insurer or the consumer of health services. This paper discusses medical portals that can provide such Web-based access to medical information and describes a three-tier Web architecture to support such access.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.061 | 0.032 |
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".