A plateau in performance does not necessarily equate to high-quality performance, so 'overcome' thresholds may misrepresent surgeon mastery¶

Claim · All claims
Evidence · 1 study · review r3 · q1
Currency · newest study 2020 · not yet checked against its sources · not reviewed by a person

Subclaims¶

q1 i? The article argues that a plateau in performance only implies a surgeon is no longer improving, not that performance is high quality, and that terms such as 'overcome' can be misnomers implying mastery the thresholds do not evidence. → Soomro N. A. 2020

Evidence¶

Soomro N. A. 2020¶

Soomro N. A., Hashimoto D. A., Porteous A. J., Ridley C. J. A., Marsh W. J., Ditto R. and Roy S. (2020). Systematic review of learning curves in robot‐assisted surgery. BJS Open. https://doi.org/10.1002/bjs5.50235

q1 · i? · review · r3

Conceptual argument in the article's introduction, offered without data: performance thresholds such as time to plateau are often simplistic and may not capture sufficient evidence about the learning process to support claims of mastery or proficiency.

"a plateau in performance does not necessarily equate to high‐quality performance; it only implies that a surgeon is no longer improving1."

Discussion¶

Related Claims¶

• Learning curve estimates in robot-assisted surgery vary substantially between studies of the same procedure and are subject to considerable uncertainty
• Methods and terminology used to define the point at which the robot-assisted learning curve is overcome are inconsistent and often lack statistical validation — related
• Mastery learning implementation causes a mastery attrition bias that confounds learning curves, and it cannot be addressed universally — related