Use of the Schelin Catheter for transurethral intraprostatic anesthesia prior to Rezūm treatment.
Bibliographic record
Abstract
Minimally invasive surgery techniques (MIST) have become newly adopted in urological care. Given this, new analgesic techniques are important in optimizing patient outcomes and resource management. Rezūm treatment (RT) for BPH has emerged as a new MIST with excellent patient outcomes, including improving quality of life (QoL) and International Prostate Symptom Scores (IPSSs), while also preserving sexual function. Currently, the standard analgesic approach for RT involves a peri-prostatic nerve block (PNB) using a transrectal ultrasound (TRUS) or systemic sedation anesthesia. The TRUS approach is invasive, uncomfortable, and holds a risk of infection. Additionally, alternative methods such as, inhaled methoxyflurane (Penthrox), nitric oxide, general anesthesia, as well as intravenous (IV) sedation pose safety risks or mandate the presence of an anesthesiology team. Transurethral intraprostatic anesthesia (TUIA) using the Schelin Catheter (ProstaLund, Lund, Sweden) (SC) provides a new, non-invasive, and efficient technique for out-patient, office based Rezūm procedures. Through local administration of an analgesic around the prostate base, the SC has been shown to reduce pain, procedure times, and bleeding during MISTs. Herein, we evaluated the analgesic efficacy of TUIA via the SC in a cohort of 10 patients undergoing in-patient RT for BPH.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".