A Patient-Centered Approach in Sensor Science: Embracing Patient Engagement for Translational Clinical Technologies
Bibliographic record
Abstract
With the goal of impacting patient quality of life and outcomes, sensor science offers significant potential to revolutionize healthcare by providing advances in the detection of molecular biomarkers for personalized clinical technologies. The sensor community has achieved significant technical advancements that can impact diagnostics, health monitoring, and disease treatment; however, many sensor innovations remain confined to the laboratory, failing to bridge the translational gap between research and real-world clinical applications. This perspective presents a new direction for the sensor community, where sensor development centers on the needs and experiences of the primary beneficiaries: the patients. We provide guidelines and resources for researchers to engage with patients early and continuously throughout the research process to inform sensor specifications and better align sensor technologies with real-world clinical needs, improving their adoption and impact. We also present examples for implementing a patient-centered approach in sensor development and planning for patient engagement in sensor research. In the design of impactful sensors for patients, researchers must expand focus beyond technical specifications to embrace a patient-centered approach, which will likely lead to new opportunities for collaboration and evolution in the sensor science community.
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.
How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".