A Simple and Reliable Health Monitoring System For Shoulder Health: Proposal
Bibliographic record
Abstract
BACKGROUND: The current health care system is complex and inefficient. A simple and reliable health monitoring system that can help patients perform medical self-diagnosis is seldom readily available. Because the medical system is vast and complex, it has hampered or delayed patients in seeking medical advice or treatment in a timely manner, which may potentially affect the patient's chances of recovery, especially those with severe sicknesses such as cancer, and heart disease. OBJECTIVE: The purpose of this paper is to propose a methodology in designing a simple, low cost, Internet-based health-screening platform. METHODS: This health-screening platform will enable patients to perform medical self-diagnosis over the Internet. Historical data has shown the importance of early detection to ensure patients receive proper treatment and speedy recovery. RESULTS: The platform is designed with special emphasis on the user interface. Standard Web-based user-interface design is adopted so the user feels ease to operate in a familiar Web environment. In addition, graphics such as charts and graphs are used generously to help users visualize and understand the result of the diagnostic. The system is developed using hypertext preprocessor (PHP) programming language. One important feature of this system platform is that it is built to be a stand-alone platform, which tends to have better user privacy security. The prototype system platform was developed by the National Cheng Kung University Ergonomic and Design Laboratory. CONCLUSIONS: The completed prototype of this system platform was submitted to the Taiwan Medical Institute for evaluation. The evaluation of 120 participants showed that this platform system is a highly effective tool in health-screening applications, and has great potential for improving the medical care quality for the general public.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".