Abstract Hydrocephalus remains one of the most common and challenging conditions in pediatric neurosurgery, with substantial morbidity and long-term healthcare burden worldwide. Endoscopic Third Ventriculostomy (ETV) has emerged as an important alternative to cerebrospinal fluid shunting; however, its success remains highly variable across patient populations. To address this variability, the Endoscopic Third Ventriculostomy Success Score (ETVSS) was developed to identify patients most likely to benefit from ETV. By incorporating age, etiology, and prior shunt history into a simple scoring system, the ETVSS has been widely adopted as a standardized framework for clinical decision-making and patient counseling. Nevertheless, accumulating evidence indicates that actual outcomes may deviate from predicted probabilities in specific clinical scenarios. Although additional predictive approaches—including machine learning models, intraoperative findings, and radiological indicators—have been proposed, most models remain dependent on selected variables rather than directly capturing the underlying pathophysiological mechanisms that determine surgical outcomes. From a mechanistic perspective, the core components of the ETVSS may be better understood as indirect representations of underlying processes rather than direct causal determinants of surgical success. In this context, the ETVSS primarily reflects the distribution of hydrocephalus subtypes within a population, rather than causal determinants at the individual level. Therefore, clinical decision-making should integrate statistical prediction with mechanistic understanding. In practice, a simplified but robust principle remains applicable: ETV is most appropriate for obstructive hydrocephalus, whereas shunting remains the preferred option for communicating hydrocephalus. Integrating mechanistic insights with predictive models may enable more accurate and individualized treatment strategies.
Hongbin Cao (Thu,) studied this question.