Wireless Integrated Biosensors for Point-of-Care Diagnostic Applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recent advances in integrated biosensors, wireless communication and power harvesting techniques are enticing researchers into spawning a new breed of point-of-care (POC) diagnostic devices that have attracted significant interest from industry. Among these, it is the ones equipped with wireless capabilities that drew our attention in this review paper. Indeed, wireless POC devices offer a great advantage, that of the possibility of exerting continuous monitoring of biologically relevant parameters, metabolites and other bio-molecules, relevant to the management of various morbid diseases such as diabetes, brain cancer, ischemia, and Alzheimer's. In this review paper, we examine three major categories of miniaturized integrated devices, namely; the implantable Wireless Bio-Sensors (WBSs), the wearable WBSs and the handheld WBSs. In practice, despite the aforesaid progress made in developing wireless platforms, early detection of health imbalances remains a grand challenge from both the technological and the medical points of view. This paper addresses such challenges and reports the state-of-the-art in this interdisciplinary field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it