『Show Me The Evidence』のカバーアート

Show Me The Evidence

Show Me The Evidence

著者： Anthony G. Gallagher Flux Learning Ltd
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Most training is sold on confidence. Show Me The Evidence is built on data. In every episode we take a single study, clinical trial, or systematic review and work through what it found, how it was designed, and what it means for the way we teach and assess skill. We focus on metrics-based training and proficiency-based progression, the approach that asks learners to demonstrate measurable competence before moving on, and we trace its results across surgical, medical, and professional education. This is a podcast for learning professionals and medical educators who want more than opinion. Expect plain-language breakdowns of the research, honest discussion of what the evidence does and does not support, and conversations with the people behind the studies. If you make decisions about how people are trained, we think you deserve to see the evidence first.2026 OGC Metrics and Anthony G Gallahger. 社会科学 科学
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  • Experience Is Not Proficiency: Measuring what surgeons actually do | Show Me The Evidence E10

    Experience Is Not Proficiency: Measuring what surgeons actually do | Show Me The Evidence E10
    2026/08/16
    In this episode, Professor Anthony G. Gallagher is joined by Dr Rui Farinha, a senior consultant urologist in Lisbon who has completed fellowships in Spain, Germany and Belgium in laparoscopic and robotic surgery, and who trained with Professor Alexandre Mottrie in Aalst and at Orsi Academy in Belgium. The two met at Orsi in 2019.The conversation begins with a practical question. Robots are expensive, so what do they actually give the surgeon? Rui sets out the case carefully: tremor filtering and motion scaling for precision, wristed instruments for dexterity in tight anatomy such as the pelvis, magnified stereoscopic vision, better ergonomics over long procedures, and a digital platform capable of recording and analysing performance. Then he adds the caveat that frames the rest of the hour. None of these technical advantages automatically produces a better clinical outcome.From there the discussion turns to how surgeons are trained. Rui traces his own path through three eras: an apprenticeship model in open surgery that depended on which cases turned up and which consultant happened to be supervising, a laparoscopic era in which he discovered that open skills did not transfer and that basic skills belonged outside the operating theatre, and a robotic era that was structured from the start around defined objectives, simulation, proximate feedback and a demonstrated standard.The heart of the episode is Rui's programme of research on robot-assisted partial nephrectomy (RAPN). He explains why he chose a technically demanding, high-risk procedure with clearly separable phases and direct consequences for the patient, and he sets out what the studies found. A complex operation can be deconstructed into observable phases, steps, errors and critical errors, with 100 per cent consensus from an international expert panel. Experienced surgeons made 69 per cent fewer total errors than novices. Within the experienced group, the low-error surgeons made 77 per cent fewer errors than the high-error surgeons, and that high-error expert group performed at roughly the level of the better novices. Procedure-specific binary metrics achieved high inter-rater reliability where a global rating scale did not. And a systematic review of partial nephrectomy training models found models widely rated as realistic and useful, but no randomised controlled trials and no evidence of skill transfer.Rui's conclusion is direct. Realism is not evidence. A simulator is a vehicle, not a training programme. Anyone building a curriculum should define the performance they want first and select or construct the simulation second, which is the opposite of what usually happens. He describes a model he developed deliberately as a delivery vehicle for a metric-based curriculum, using readily available animal tissue and emulating eight of the eleven phases of the human procedure, on the grounds that the useful question is not how realistic a model looks but how much high-quality measurable practice it permits.The episode closes on proficiency-based progression in the skills laboratory and in the operating room, on the value of proximate human feedback in an era of AI-delivered training, and on a central principle: the manufacturer's instructions for use teach the surgeon how the robot functions, while PBP determines whether the surgeon can use it proficiently.Key Topics CoveredWhat the robot actually adds, and what it does not | 0:07Guest introduction: senior consultant urologist in Lisbon, fellowships in Spain, Germany and Belgium, met Professor Gallagher at Orsi Academy in 2019Robots do not replace the surgeon and do not operate independently; they act as an interface that translates the surgeon's movementsPrecision through filtering of physiological tremor and scaling of movement, valuable in delicate dissection, suturing and vascular anastomosis | 1:46Dexterity, vision and endurance | 2:34Wristed instruments provide additional degrees of freedom where rigid laparoscopic instruments cannot, which matters most in narrow spaces such as the pelvisMagnified high-definition three-dimensional view improves depth perception and tissue plane discrimination; the surgeon controls the camera directly | 3:25An adjustable console reduces muscular strain and fatigue, supporting concentration and consistency through long procedures | 4:14The digital platform, and the caveat that matters | 5:04Because the surgeon's actions pass through a computer-controlled interface, robotic systems can record instrument movement, analyse technical performance and integrate imaging or navigationRui's caveat: these technical advantages do not automatically produce better clinical outcomes. Performance still depends on training, case selection, team coordination and appropriate use of the technologyProfessor Gallagher returns to the Dwight Meglan episode and the view that the surgeon, not the platform, makes the decisions | 5:53Three training eras: apprenticeship, laparoscopy, ...
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  • The end of "see one, do one, teach one" — Prof Stefano Puliatti | Show Me The Evidence Episode 9

    The end of "see one, do one, teach one" — Prof Stefano Puliatti | Show Me The Evidence Episode 9
    2026/07/31

    Professor Stefano Puliatti is Professor of Urology at the University of Modena, a practising robotic surgeon, a faculty member of Surgquest, and the former Medical Director of Orsi Academy in Belgium, where much of the PBP research discussed here was carried out.


    Key takeaways

    • Training quality should not depend on the luck of being assigned a gifted trainer. A shared methodology makes good outcomes reproducible.
    • Errors, not speed or step count alone, are the real indicator of surgical quality. Process measures on their own do not guarantee it.
    • A proficiency benchmark set at expert level is reachable by nearly all trainees, and usually faster than conventional training allows.
    • Good simulation does not have to be expensive. A validated methodology on a low-cost model can outperform costly kit used without one.
    • Diluting the method, for example by dropping the pre-lab benchmark, measurably slows learning and raises cost.


    Evidence and further reading

    The figures cited in this episode come from the peer-reviewed studies below. Please confirm the exact papers you want listed for this episode before publishing.

    • De Groote R, et al. Proficiency-based training and evidence-based methodology: a systematic review and meta-analysis. BJU International. doi:10.1111/bju.70333
    • Mazzone E, Puliatti S, et al. A Systematic Review and Meta-analysis on the Impact of Proficiency-based Progression Simulation Training on Performance Outcomes. Annals of Surgery, 2021. PMID: 33630473
    • Puliatti S, et al. Can all surgical trainees be trained to proficiency for a robotic urethro-vesical anastomotic task using a chicken model? A prospective, randomized trial. PMID: 40351291
    • Randomised trial on the economic impact of proficiency-based progression versus conventional robotic surgical training. PMC12907777
    • Development and validation of the objective assessment of robotic suturing and knot tying skills for a chicken anastomotic model. Surgical Endoscopy, 2020. doi:10.1007/s00464-020-07918-5
    • De Groote R, et al. Proficiency-based progression training for robotic surgery skills training: a randomized clinical trial. BJU International, 2022. doi:10.1111/bju.15811


    Credits

    Show Me the Evidence is hosted by Professor Anthony G. Gallagher and produced by Flux Learning.

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    51 分
  • From Physics-Based Simulation to Surgical Robots| Show Me The Evidence E8- Dr Dwight Meglan

    From Physics-Based Simulation to Surgical Robots| Show Me The Evidence E8- Dr Dwight Meglan
    2026/07/18
    Show Me the Evidence, Episode 8Guest: Dr Dwight Meglan Topic: Physics-Based Simulation, Surgical Robotics and Why Simulators Still Don't Measure What MattersEpisode SummaryIn this episode, Professor Tony Gallagher is joined by Dr Dwight Meglan, the engineer who developed one of the first physics-based virtual reality simulators for endovascular procedures in the late 1990s, and who has spent the last two decades building surgical robots. Together they trace 30 years of simulation-based training and ask why so little has changed: simulators are still not verified against real-world physics, the field still measures process rather than skill, and device manufacturers, not educators, still set the agenda. The conversation ranges from haptics and instrumented torquers to Likert scales, credentialing committees, telesurgery risk, and why autonomous surgical robots are a regulatory and financial impossibility rather than a technical one. It closes with the evidence for Proficiency-Based Progression (PBP) and the leadership needed to adopt it.Key Topics Covered1. Building the first physics-based VR simulators | 0:00Meeting at Medicine Meets Virtual Reality in the late 1990sReal-time physics of tool and tissue interaction, fluoroscopy and haptic feedbackWhy the goal was to replicate reality, not design a user experiencePhysics tests to verify simulator correctness still do not exist, 25 years on2. Who really drives simulation: the device manufacturers | 2:24Manufacturers pay for simulation, so manufacturers shape itTraining to use the device versus training to perform the procedureSimulators in exhibition booths: marketing tools first, training tools second3. Haptics and the sensory threshold problem | 3:55The instrumented torquer: measuring what cardiologists actually feelJust noticeable difference thresholds vary between cliniciansStill no published datasets on the forces a cardiologist feels during catheterisationClinicians praising the haptics on simulators where the haptics were switched off4. Using devices safely: the stapler and the defibrillator | 8:52A stapling device with a 6 to 27 per cent leak rate, where one third of patients who develop a leak dieTraining to the instructions for use is device safety training, not surgical skills trainingCardiac defibrillator implantation: clinicians departing from the instructions for useConstruct validity findings: some very senior clinicians perform worse than the worst trainee when assessed with objective, peer-derived metrics5. What should a simulator measure? | 13:19The original approach: record everything, then find the measures that matterMetrics for mechanical thrombectomy for acute stroke, developed from the human procedure with MenticeHow clinician-led metrics forced a redesign of contrast injection, later patentedIt works when you insist on it, but you must start with the metrics6. The state of simulation metrics today | 17:35At a recent conference, almost none of the exhibited simulators had any metrics at allSome manufacturers avoid measurement deliberately: plausible deniabilityValidated metrics as a purchasing condition: if you cannot build them in, we will not buy7. From simulation to surgical robotics | 21:52Why simulation felt like a capped market and robotics did notThe analogous DNA of simulators and robots as real-time information processing systemsVerification versus validation: robots are bench-tested against dozens of specifications, simulators almost never are8. Measuring process, not skill | 26:49Motion tracking and path length: lessons not learned from laparoscopic surgeryAI-driven pattern hunting as a fishing expedition without a hypothesisSuturing as the test case: the physics of tissue apposition has never been publishedPhysical intelligence and humanoid robotics will improve simulation from the outside in9. Likert scales are not measurement | 36:47Binary, procedure-specific metrics require scoring the entire video, reliably, in pairsOne-to-five ratings after watching a few minutes of video are hand waving, not assessmentRing exercises on robotic simulators have never been verified against real forces10. Whose job is it? Credentialing and privileging | 39:27Manufacturers certify device use; professional societies and hospitals grant privilegesPer-procedure privileging in the United States versus broad qualification in EuropeThe credentialing committee problem: standards set by the least experienced memberCase volume, fellowship length and reputation are social proof, not evidence of competence11. A jumbo jet a day: the human cost | 44:43Deaths from surgical skills deficits estimated as equivalent to a full jumbo jet crashing every day, consistent with evidence that around 4.2 million people die within 30 days of surgery each year (Nepogodiev et al., The Lancet, 2019)Why one death at a time never makes headlines the way one crash doesThe Bristol Royal Infirmary case: peers knew for a decade before the front pages forced action (The ...
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