摘要：我们介绍了 MoCha 2026 帕金森步态基准与挑战赛的冠军参赛方案。该方案根据训练期间未见临床站点采集的规范化 SMPL 动作数据，预测 MDS-UPDRS 步态严重程度。系统在隐藏测试集上达到 0.6945 的宏平均 F1，在 58 个参赛方案中排名第一，高于得分为 0.5807 的第二名和得分为 0.4289 的主办方基线；其模型基于冻结的公开动作编码器，并仅使用单个 $4\times512$ 线性层。几乎全部领先优势都来自通常被视作事务性处理的三个阶段：精确复现参考基准的分类头配置；在主办方提供的受试者分组内，对每次步行的后验概率进行平均；以及针对特征均值和决策工作点进行无标签传导式校准。对编码器进行微调的四种不同形式均导致性能下降，另外十种替代编码器的表现也更差。由于每个消融实验结果都需要在隐藏测试集上付费获取，我们的全部消融数据均来自该测试集；而我们自己进行的“留出两个队列”交叉验证显示，在 11 种配置中，其结果与决定最终排名的分数呈负相关。我们完整呈现了这些负面结果，并指出我们最大的收益——受试者级聚合——构成了该基准上的性能上限约束。

Abstract:We describe the winning entry to the MoCha 2026 Benchmark and Challenge on Parkinsonian Gait, which predicts MDS-UPDRS gait severity from canonicalized SMPL motion recorded at clinical sites unseen during training. The system reaches 0.6945 macro-F1 on the hidden test and ranked first of 58 entries, ahead of the runner-up at 0.5807 and the organizers' baseline at 0.4289, on a frozen public motion encoder with a single $4\times512$ linear layer. Nearly all of the margin comes from three stages usually treated as bookkeeping: reproducing the reference benchmark's exact head recipe, averaging per-walk posteriors within the subject grouping the organizers ship, and a label-free transductive calibration of the feature mean and the decision operating point. Fine-tuning the encoder lost in four distinct forms, and ten alternative encoders were worse. Every ablation number is a paid read on the hidden test, because our own leave-two-cohort-out cross-validation proved anti-correlated with the deciding score over eleven configurations. We give the negative record in full, and identify our largest gain, subject-level aggregation, as the binding ceiling on this benchmark.