<![CDATA[Recent empirical studies highlight that post-factum clinical forensic records in sexual violence cases remain underutilized, leaving emergency case handling heavily dependent on intuitive or first-come, first-served administrative practices that delay critical interventions. This study aims to formulate and validate an automated decision support model utilizing the Simple Additive Weighting (SAW) algorithm to structure medicolegal data into an objective priority-ranking mechanism for case handling. Practically, this framework enables public hospitals and victim protection units to optimize scarce emergency resources, safeguard golden hours for medical prophylaxis, and execute immediate safe-house evacuations. The research method extracted five clinical-forensic criteria—victim age vulnerability, physical and or genital trauma severity, perpetrator relationship, reporting delay, and toxicological or weapon threats from 50 de-identified clinical forensic records, normalized through linear cost-benefit functions weighted by forensic specialists. The results demonstrate robust algorithmic stability across varying weight scenarios, with Spearman's rank correlation reaching and , alongside an empirical expert concordance of () that successfully categorized 22% of cases into Emergency Handling Priority I. The research completely meets the expected computational precision and validity, confirming that the model provides a transparent handling prioritization mechanism. However, further multi center trials and integration with natural language processing tools are needed to automate textual feature extraction in larger hospital settings.]]>
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