Intelligent Ureteral Stent for Early Detection of Hydronephrosis
Bibliographic record
Abstract
Abstract Millions of people around the world currently suffer from kidney stone diseases. While ureteral stenting is an unmistakably effective treatment of these patients, their long‐term adverse effects can result in the build‐up of crystals around the stent. This, in turn, can lead to new ureter blockages that can dangerously increase kidney pressure, a condition known as hydronephrosis, which, if severe and prolonged, can cause irreversible kidney damage. Toward enabling early detection of hydronephrosis, this paper investigates the first intelligent ureteral stent with an integrated radiofrequency antenna and micro pressure sensor for resonance‐based wireless tracking of kidney pressure. Prototyping is conducted using a commercial ureteral stent as the substrate for microfabrication of the device. The packaged device is experimentally assessed for electrical characterizations and wireless pressure sensing using an in vitro test model. Preliminary telemetry testing demonstrates the fundamental ability of the device with its approximately linear responses of up to 1.7 kHz mmHg−1 over a pressure range of up to 120 mmHg in air, water, and artificial urine. These findings verify the efficacy of the device design and the approach to kidney pressure monitoring through indwelling stents, paving the way for the transfer of this technology to today's ureteral stent products.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".