Bibliographic record
Abstract
Abstract – Introduced by the Ontario Telemedicine Network, the telehomecare program provides monitoring of patient parameters and training sessions. Telehomecare is a sub-field of telehealth that influences the delivery of health services to patients who use telecommunications technology and is often accelerated with long-distance patient distance in realtime. Telehomecare offers improved access to home care at lower costs and promises unique opportunities for home care patient empowerment and improved care outcomes. The telehomecare method that is designed is that there are various sensors that are used to find out biomedical data, the data is then converted by the sensor to a certain amount and sent to the process section, after processing the data is sent to the webserver with an intermediate wifi module that was previously connected to the internet, on the webserver the measured data can be observed through the website. The main purpose of making this telehomecare system is to make health monitoring tools that can be easily accessed through the website, used in patients who have conditions must be under intensive supervision at home. Data monitored is body temperature, pulse, body position using input from temperature sensors, pulsemeter sensors and accelerometer sensors. In this tool using microcontrollers, namely Arduino Uno and Esp 8266 W-iFi module (NodeMCU) which have been programmed to send sensors input data to the web server data sent to the web server will be displayed in a website interface. The database used as a webserver on this system is Firebase, because it is a realtime database. The results obtained from this study, for reading the desired health parameters can be read, were temperature sensor readings that reached 99.29%, and pulsemeter sensor readings which reached 99.178%. Keywords : Telehomecare, Website, Arduino Uno, NodeMCU, Webserver.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.029 | 0.006 |
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".