## 7 Results and Performance Analysis
### Multilingual Transfer and Zero-Shot Generalization
The ablation confirms that semantic parsing, while not always strictly necessary for final classification accuracy, is indispensable for producing interpretable and trustworthy outputs in high-stakes clinical settings. For instance, in a case where the input described a 70-year-old man experiencing dizziness, slurred speech, and right arm weakness, classic signs of stroke, the ablated model still predicted \"activate stroke protocol\" as the correct intervention. However, without the semantic parsing head, it failed to extract and represent these symptoms explicitly in the EUREKA frame. This lack of structured reasoning meant the system could not highlight why the decision was made, undermining interpretability. In a clinical setting, this would hinder a clinician's ability to verify or override the system's recommendation and make it impossible to log structured evidence for audit or downstream integration. Ablation Study 3. Without Intervention Classification Head. In this experiment, we removed the intervention classification head, allowing the model to perform only semantic parsing without producing a direct recommendation. While the EUREKA frame remained intact, accurately capturing symptoms, onset time, and urgency, the absence of an explicit intervention prediction disrupted the system's ability to complete the decision-making loop. Performance metrics reflect this gap: the model retained a high Slot F1 score of 0.90 and a Frame F1 of 0.88, indicating that the extraction of structured medical information was largely unaffected. However, since no intervention output was available, both the Macro F1 (intervention) and accuracy (intervention) scores were undefined. This ablation demonstrates that while semantic understanding can be preserved, the classification head is essential for actionable triage. Without it, clinicians would need to manually interpret the structured outputs, introducing delay and variability in emergency response. The classification head thus plays a pivotal role in transforming understanding into timely and standardized clinical decisions. In a stroke-related example, a 66-year-old woman presented with slurred speech, facial drooping, and sudden weakness in her right arm. The system, even without the intervention classification head, correctly extracted and structured these symptoms into the EUREKA frame, identifying the urgency as \"high.\" However, without the classification module, the system failed to map this structured understanding to the correct medical action, namely, \"activate stroke protocol.\" Consequently, no clear directive was delivered to the remote clinician. While the semantic structure was accurate, the lack of an explicit intervention recommendation delayed the clinical response in a time-critical scenario where minutes count. Ablation Study 4. Without Knowledge Encoding Module. In this case, we removed the ontology-based knowledge encoding and constraint enforcement mechanisms from the system. These components normally act as structural filters that validate the semantic and classification outputs against domain knowledge, ensuring that the model's predictions remain medically consistent and clinically plausible. Without these constraints, the system had access to the same training data and model parameters but was permitted to produce unconstrained outputs. As a result, we observed an increase in noisy or illogical predictions. For example, in one case, the model assigned the symptom \"acute chest pain\" a low urgency level and incorrectly linked it to a non-emergency action such as \"recommend rest,\" despite the presence of metadata indicating cardiovascular risk. This type of mismatch would not have passed the validation layer in the full model. Performance-wise, the model without knowledge constraints yielded a Slot F1 of 0.86 and a Frame F1 of 0.80, both lower than the full model's scores of 0.91 and 0.89, respectively. Intervention prediction was also affected, with a Macro F1 of 0.77 and an accuracy of 0.83, compared to 0.88 and 0.92 in the complete system.