Ureteroscopic ultrasound technology to size kidney stone fragments: Proof of principal using a miniaturized probe in a porcine model.
Bibliographic record
Abstract
Background: the ability to measure stone fragment size could help prevent attempting to extract too large a stone fragment. We evaluated the ability of a 1.2 mm (3.6 French) ultrasound probe to measure stone fragments in a porcine kidney. Methods: 15 human stones of three types (five each calcium oxalate, cystine, calcium phosphate) sized 3–7 mm were placed deep in a porcine kidney collecting system. The sound speed of each stone type was determined using a separate reference stone. A 2 MHz, 1.2 mm needle hydrophone was used to send and receive ultrasound pulses. Stone thickness was calculated as d=c*t/2 by determining the signal transit time through the stone, t, and the stone sound speed, c. Calculated stone thicknesses were compared to digital caliper measurements. Results: Stone size was determined for all 15 stones. Correlation between ultrasound-determined thickness and caliper measurements was excellent (r2=0.90, p<0.0001) with ultrasound performing well in all three stone types. All stone measurements were accurate within 1 mm, and ten (66%) stone measurements were accurate within 0.5 mm. Conclusions: Ultrasound-based measurements are accurate and precise using a 3.6 French probe with stone fragments placed deep in a porcine kidney. [Work supported by Grants NIH DK43881 and NSBRI SMST01601.]
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".