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Record W1905271526 · doi:10.5489/cuaj.45

Robotic radical prostatectomy: Fools rush in, or the early bird gets the worm?

2012· article· en· W1905271526 on OpenAlexaffvenueabout
Laurence Klotz

Bibliographic record

VenueCanadian Urological Association Journal · 2012
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineProstatectomyRobotic surgeryChinSurgeryBlood lossGeneral surgeryProstate

Abstract

fetched live from OpenAlex

Robotic prostatectomy seems to be here to stay. Currently, over 50% of radical prostatectomies (RPs) in the United States are done robotically. The article by Dr. Chin and colleagues1 provides a perspective on this trend. Robotic technology represents a challenge to our Canadian system, yet it has rapidly spread throughout the US (where there are over 350 robots). With the da Vinci, the advantages of magnification and binocular vision, 6 or more degrees of freedom, and reduced fatigue (surgeons sit during the procedure) are apparent. Demonstrating an improved outcome should only be a matter of time, one would think. Yet, compared with open surgery, the data so far show no clear evidence of improved oncologic outcome or side effect profile, despite the decreased blood loss and reduced transfusion requirements of robotic surgery (compared with open surgery, not conventional laparoscopic surgery). However, the duration of hospital stay, analgesic requirements, time off work and recovery seem relatively similar. The robotic approach, however, needs more advantages to justify the cost. Improved outcome may be difficult to demonstrate. Variation in outcome with respect to PSA recurrence, potency sparing and incontinence varies a great deal, even among high-volume surgeons. These variations seem to be more dependent on the surgeons, as opposed to the different techniques of accessing the prostate.2 The experience reported by Chin and colleagues supports this observation. The results are acceptable, but not stellar: a positive margin rate of 30%, 10% of patients with moderate and 20% with mild stress incontinence, an average hospital stay of 3.5 days and an average of 12 days with an indwelling catheter. These results will likely improve over time, but the results of an experienced surgeon doing open radical prostatectomy are a high bar. Another difficult issue in the rush toward robotic prostatectomy is the volume-quality outcome relation. The outcome is best when the surgeon's annual volume is 50 cases or more. Given the cost of the da Vinci and the length of the learning curve,3 it makes sense that robotic prostatectomy would be performed in high volumes by a few surgeons at centres of excellence. In attempting to maintain market share, every hospital in the US, large or small, is motivated to acquire the device. Nonetheless, 90% of US urologists who do RP perform fewer than 10 annually. Additionally, once these units are acquired, they take on their own mandate: “Use Me,” resulting in the unseemly marketing of robotic prostatectomy as a minimally invasive procedure with no morbidity. Horsefeathers. Minimal access does not mean minimally invasive, and the morbidity reported to date is comparable to the open approach. One hopes that in Canada we will demonstrate some maturity in our approach to this expensive device. A few robots (perhaps 5–10) should be purchased by acknowledged centres of excellence across the country, where a limited number of surgeons would maintain a high volume of cases and quickly develop expertise. Further dispersion of the robot beyond these centres should await solid data showing improvement in clinically significant oncologic and quality of life related outcomes.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.004
Scholarly communication0.0030.009
Open science0.0010.001
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0060.003

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.

Opus teacher head0.023
GPT teacher head0.253
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations14
Published2012
Admission routes3
Has abstractyes

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