PD24-10 EVALUATION OF THE LEARNING CURVE FOR THULIUM LASER TRANSURETHRAL VAPORESECTION OF THE PROSTATE (THUVARP)
Bibliographic record
Abstract
You have accessJournal of UrologyBenign Prostatic Hyperplasia: Surgical Therapy & New Technology II1 Apr 2016PD24-10 EVALUATION OF THE LEARNING CURVE FOR THULIUM LASER TRANSURETHRAL VAPORESECTION OF THE PROSTATE (THUVARP) Ala'a Sharaf, jo worthington, and Hashim Hashim Ala'a SharafAla'a Sharaf More articles by this author , jo worthingtonjo worthington More articles by this author , and Hashim HashimHashim Hashim More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.1775AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES Transurethral resection of the prostate (TURP) has been the standard operation for voiding LUTS for 40 years with very few changes. It is generally a very successful operation but has well documented risks for the patient. Various laser techniques have become available but none have become widely used in the National Health Service (NHS) because of lengthy training required for surgeons or inferior performance on clinical outcomes. The thulium laser technique (ThuVARP) vaporises and resects the prostate using a surgical technique similar to TURP, facilitating a potentially shorter training period for surgeons. A systematic review of laser technology recently recommended ThuVARP as an acceptable alternative to TURP for the treatment of symptomatic benign prostatic obstruction (BPO). For patients undergoing BPO surgery, NICE clinical guidelines recommended offering TURP or holmium laser enucleation (HoLEP). However, HoLEP is only used in a few centres due to the steep learning curve. The Objective was to assess the surgical learning curve of ThuVARP, as part of a prospective, randomised, multicentre, controlled trial to determine the clinical and cost effectiveness of ThuVARP versus TURP in the NHS (UNBLOCS trial). METHODS The UNBLOCS trial is funded by the NIHR HTA program. Consultant urologists were mentored to perform ThuVARP. All participating surgeons observed the chief investigator performing 1 to 2 cases. The lead surgeon then observed the principal investigators (PIs) performing 2 to 5 cases. The surgeons then performed cases without supervision. Competency was assessed with the Intercollegiate Surgical Curriculum Programme work-based assessments (ISCP-WBA) by an independent assessor and the PIs were signed off once the competency criteria were met. RESULTS A total of 9 surgeons were involved form 6 different centres (3 district general hospitals and 3 tertiary referral centres). All of the surgeons have performed at least 150 TURPs. A mean of 2.1 cases were observed by each surgeon and a mean of 2.2 cases were performed by each surgeon under supervision. A mean of 7 cases were performed by the PIs before being signed off as competent. CONCLUSIONS The study has shown that ThuVARP has a short learning curve not exceeding 12 cases for surgeons already experienced in performing TURPs. Results of the non-inferiority trial are awaited to see if outcomes are comparable to TURP, making it a feasible alternative with a short learning curve. © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e515 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Ala'a Sharaf More articles by this author jo worthington More articles by this author Hashim Hashim More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...
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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 | 0.043 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.034 | 0.009 |
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".