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We evaluate 12 open-weight vision-language models (VLMs) on binary classification across two clinical neuroimaging cohorts, \\textsc{FOR2107} (affective disorders) and \\textsc{OASIS-3} (cognitive decline). Both datasets come with structural MRI data that carries no reliable individual-level diagnostic signal. Under these conditions, smaller VLMs exhibit gains of up to 58\\% F1 upon introduction of neuroimaging context, with distilled models becoming competitive with counterparts an order of magnitude larger. A contrastive confidence analysis reveals that merely \\emph{mentioning} MRI availability in the task prompt accounts for 70-80\\% of this shift, independent of whether imaging data is present, a domain-specific instance of modality collapse we term the \\emph{scaffold effect}. 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A contrastive confidence analysis reveals that merely \\emph{mentioning} MRI availability in the task prompt accounts for 70-80\\% of this shift, independent of whether imaging data is present, a domain-specific instance of modality collapse we term the \\emph{scaffold effect}. 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