A dissemination-based mobile web application framework for juvenile ideopathic arthritis patients
Bibliographic record
Abstract
Adopting mobile technologies in assisting healthcare is opening new possibilities in medical health domain through bringing a dramatic shift from conventional paper-based tracking to electronic tracking and evaluation system. Health information systems have the potential to offer greater improvement in collecting and accessing relevant information, disseminating data among health practitioners and patients in a reliable and secure manner with faster speed and analyzing them efficiently. In this paper we have proposed and implemented a prototypical client-server based health information framework that allows both clinicians and patients to send data to a centralized backend system database and have access to those data when needed. Our framework adopts mobile electronic pain diary named PInGO for juvenile idiopathic arthritis patients to report their health conditions to the clinicians. Our framework offers secure, reliable and fast dissemination and access of these data through leveraging the latest push-based Web technology and RESTful web services. From our preliminary experiment results it has been observed that data dissemination using RESTful web services within event-based publish-subscribe domain provides greater performance improvement comparing to traditional pull-based data dissemination over Web.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".