Internet of Things in Telemedicine: a Discussion Regarding to Several Implementation
Bibliographic record
Abstract
With the increase of health requirement, the idea of telemedicine turns in to reality. By the help of effective audiovisual and data communication, the process of practice of medical care that includes the delivery of medical care, consultation and treatment, diagnosis, transferring of medical data as well as health education is termed as Telemedicine [1]. Most of the actual implementation of telemedicine system is done by traditional video conference tool, which is somehow becomes not very supportive as far as the complex medical activities are concerned. So this paper discusses the effects of different implementation regarding to telemedicine by the internet of things. There are many ways of telemedicine which are implementing according to the available sources of Internet of things (IoT), the idea of this paper is to highlight those ways and discusses that how much these available procedures are useful for the remote areas. Comparison will be done as a conclusion of this paper
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".