Bibliographic record
Abstract
The recent advancement in mobile technology has established smartphones and tablet devices as the consumer device nodes to access the Electronic Health Records (EHR). Mobile devices further aid the healthcare professionals to access the EHR on the go and outside a centralized health facility. However, the over reliance on wireless communication mediums (e.g., Wi-FI, and 3.5G/4G) by mobile devices hampers the reliable flow of disseminating the EHR. For instance, there is no guarantee that the medical data from the main Health Information System (HIS) can be consumed on the mobile device of a healthcare professional when there is no connectivity. While a secure caching technique of the medical data on the mobile can be a solution to facilitate offline accessibility, the same technique can lead to challenges of data conflict. Specifically, when the cached data is updated in an offline mode and that information has to be synchronized with the HIS. We investigate efficient means of disseminating the EHR in unreliable networks. Our mobile architectural design consists of mobile nodes, a cloud-hosted middleware and the HIS. The proposal of the middleware is to enforce provenance, services composition, and reliable synchronization of the medical data for faster dissemination. The preliminary evaluations of the proposed approaches show high performance boost in terms of latency optimization and reliability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".