In Situ and Intraoperative Detection of the Ureter Injury Using a Highly Sensitive Piezoresistive Sensor with a Tunable Porous Structure
Bibliographic record
Abstract
Iatrogenic ureteral injury, as a commonly encountered problem in gynecologic, colorectal, and pelvic surgeries, is known to be difficult to detect in situ and in real-time. Consequently, this injury may be left untreated, thereby leading to serious complications such as infections, renal failure, or even death. Here, high-performance tubular porous pressure sensors were proposed to identify the ureter in situ intraoperatively. The electrical conductivity, mechanical compressibility, and sensor sensitivity can be tuned by changing the pore structure of porous conductive composites. A low percolation threshold of 0.33 vol % was achieved due to the segregated conductive network by pores. Pores also lead to a low effective Young’s modulus and high compressibility of the composites and thus result in a high sensitivity of 448.2 kPa–1 of sensors, which is consistent with the results of COMSOL simulation. Self-mounted on the tip of forceps, the sensors can monitor tube pressures with different frequencies and amplitudes, as demonstrated using an artificial pump system. The sensors can also differentiate ureter pulses from aorta pulses of a Bama minipig in situ and in real-time. This work provides a facile, cost-effective, and nondestructive method to identify the ureter intraoperatively, which cannot be effectively achieved by traditional methods.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| 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".