Bibliographic record
Abstract
The employment of mobile devices as the data consumption node in the medical domain (known as mHealth) is gaining widespread adoption since mobile devices facilitate remote and ubiquitous access to medical data. Today, it is a common phenomenon to see medical practitioners who own multiple mobile devices such as smart phones and tablets and expect to experience application consistency across the multiple devices. However, this expectation is hampered by the fact that mobile devices rely on wireless communication mediums which can experience sporadic disconnections. What is even challenging is the presence of the CAP theorem which states that considering the following three properties of a distributed system: consistency, availability, and partition tolerance, only two of the properties can be achieved simultaneously. In an ongoing research collaboration with the Geriatrics Ward at the City Hospital, Saskatoon, Canada, we deployed a reliable mHealth architecture that enables healthcare practitioners to use their n-mobile devices to access medical records. We proposed a brokerage platform that synchronizes the medical data on the multiple devices with careful consideration to the CAP theorem. Our proposed mHealth architecture is evaluated and the result in the real-world shows high support for scalability, real-time medical data propagation, and high capacity offline storage.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".