Service learning in dental education: enhancing student competence through an oral health training programme for children with autism spectrum disorder.

Randomized controlled trials (RCTs) are considered as the gold standard for intervention evaluation, yet treatment crossover frequently undermines their validity. In surgical RCTs, non-compliance rates tend to exceed those in pharmacological studies due to procedural complexity, surgeon expertise, and patient-related factors []. Recent meta-analyses have reported crossover rates of 7–13% in cardiac surgery RCTs [], a mean of 12.6% (range 0–45%) in gastrointestinal cancer trials [], and 8–42% in spine surgery trials (Supplemental Table S1) []. In the Spine Patient Outcomes Research Trial (SPORT), 43% of patients crossed over from conservative treatment to surgery and 42% vice versa within 24 months []. Similarly, in the anterior cruciate ligament surgery necessity in non-acute patients (ACL SNNAP) trial, 41% of patients allocated to rehabilitation crossed over to surgical reconstruction within 18 months, and 28% assigned to surgery did not receive their allocated intervention []. Conventional approaches to non-compliance with randomization exhibit critical limitations [, ]. Intention-to-treat (ITT) analysis preserves randomization integrity but attenuates effect estimates by diluting results with non-compliant participants. Per-protocol (PP) and as-treated (AT) analyses introduce bias by violating randomization principles. While methods like regression adjustment, propensity score matching, or inverse probability weighting can mitigate bias from measured confounders, they require complete confounder measurement and correct model specification. Instrumental variable (IV) methods leverage randomization to address unmeasured confounding under strict assumptions (Fig. ), yet often yield imprecise estimates in surgical settings where these assumptions are frequently violated [].