High-frequency jet ventilation is beneficial during shock wave lithotripsy utilizing a newer unit with a narrower focal zone
Bibliographic record
Abstract
INTRODUCTION: High-frequency jet ventilation (HFJV) during shock wave lithotripsy (SWL) has been reported using older lithotripsy units with larger focal zones. We investigated how HFJV affects the clinical parameters of SWL using a newer lithotripsy unit with a smaller focal zone. METHODS: We reviewed all patients who underwent SWL by a single surgeon (KVA) from July 2006 until December 2007 with the Siemens Lithostar Modularis (Siemens AG, Erlangen, Germany). Either HFJV or conventional anesthetic techniques were used based on the anesthesiologists' preference. Preoperative imaging was reviewed for stone size, number and location. Total operating room time, procedure time, number of shocks and total energy delivery were analyzed. Postoperative imaging was reviewed for stone-free rates. RESULTS: A total of 112 patients underwent SWL with 80 undergoing conventional anesthesia, and 32 with HFJV. Age, body mass index, preoperative stone size and number were not significantly different between the groups. The HFJV group required significantly less total shocks (3358 vs. 3754, p = 0.0015) and total energy (115.8 joules vs. 137.2 joules, p = 0.0015). Total operating room time, SWL procedure time and postoperative stone-free rates were not significantly different. CONCLUSIONS: Previous studies using older SWL units with larger focal zones have demonstrated that HFJV can be effective in reducing total shocks and total energy. Our data is consistent with these studies, but also shows benefit with newer units that have narrower focal zones.
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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 | 0.003 |
| 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) | 0.001 | 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".