Insights on High-Value Procedures From the ISASS 4-Part Webinar Series on Current and Emerging Techniques in Endoscopic Spine Surgery Based on Surgeon Experience

Shown are exemplary plots of a polytomous Rasch partial agreement analysis to assess spine surgeons’ level of endorsement of 5 test items (patient outcomes, comfort with the procedure, instruments, patient factors, and rehabilitation) regarding a commonly performed lumbar decompression surgery. Shown is the resulting Wright plot on the left. The blue horizontal bars correspond to the responding surgeons’ latent traits written in logits (log odds) as estimates of true intervals of item difficulty and surgeon ability. The surgeons represented by horizontal bars at the top indicated a higher level of endorsement for the individual test components (positive logits) than those on the bottom (negative logits). On the right, the higher-level endorsement items are listed at the top vs the easier-to-agree-on items on the bottom. Each item may be visually inspected using its item characteristic curve (ICC) to assess the alignment between anticipated and actual values. An exemplary ICC plot is displayed in the top right graph for comfort and familiarity with the tested spine surgery. The dots graphically denote the average response of individuals in each class interval, while the solid blue curve represents the expected values predicted by the Rasch model. The corresponding person-item map (bottom right) shows the logarithmically transformed person and item positions on a unified continuum using the logit measurement unit, transitioning ordinal data to equal-interval data. This method charts both person and item positions (in logits) along the x-axis. Within Rasch modeling, these values are labeled as “locations” rather than “scores.” A surgeon’s logit location indicates their natural log odds of agreement with a series of items. Individuals with pronounced adherence to the considered attitude affirm items favorably, positioning them further to the right on the scale. The solid dots indicate the mean person location scores. The items “comfort level with the exemplary lumbar decompression procedure” and “patient-related factors” were the easiest to agree on. These items also had the smallest spread of logit locations. The most challenging item to agree on was “clinical outcomes” and “postoperative rehabilitation.” This type of Rasch analysis can expose more intense partial agreement with a test item—in this case, a commonly performed lumbar spinal decompression procedure. The person-item maps also illustrate that items were reasonably well distributed. However, some surgeons could not be measured as reliably as the majority by this set of items, indicating the test items were either too intense or not intense enough for them. The red circles highlight these areas. The analysis also showed disordered thresholds of endorsement for the 5 test items shown in this exemplary plot, suggesting that surgeons had difficulty consistently discriminating between response categories ranging from strongly disagree (1), disagree (2), agree (3), and to strongly agree (4)—a problem observed when there are too many response options (all disordered items shown in red). Examining the order and location of these test items reveals an uneven distribution of the ranked order of item difficulties or intensities along the logit continuum illustrating the true complexity of real-world surgical decision-making—data that should be integrated into traditionally developed clinical guidelines to keep the up-to-date.