The Potential Implementation of Radio-Frequency Identification Technology for Personal Health Examination and Monitoring
Bibliographic record
Abstract
This paper presents several possible applications of the radio-frequency identification (RFID) technology for personal health examination and monitoring. One application involves using RFID sensors external to the human body, while another one uses both internal and external RFID sensors. Another application involves simultaneous assessment and monitoring of many patients in a hospital setting using networks of RFID sensors. All the assessment and monitoring are done wirelessly, either continuously or periodically in any interval, in which the sensors collect information on human parts such as the lungs or heart and transmit this information to a router, PC or PDA device connected to the internet, from which patient's condition can be diagnosed and viewed by authorized medical professionals in remote locations. Instantaneous information allows medical professionals to intervene properly and in a timely fashion to prevent possible catastrophic effects to patients. The continuously assessed and monitored information provides medical professionals with more complete and long-term studies of patients. The proposed ideas promise to result in not only enhancement of the health treatment quality but also in significant reduction of medical expenditure.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".