This draft sketches a plausible epidemiologic workflow: linking acute heat exposure metrics (NOAA station data) with short-term health outcomes (emergency call logs) and tract-level vulnerability covariates, analyzed via a time-stratified case-crossover design. That general design is appropriate for transient exposures and acute outcomes, and the stated result—stronger signals in tracts with low tree cover and high overnight temperatures—is directionally consistent with existing heat-health literature, lending face validity. The framing about using an AI “sidekick” to surface and stress-test assumptions early is also a defensible meta-contribution. However, the excerpt is far too underspecified to judge the empirical claim. Key elements are missing: outcome definition and validation (are calls a proxy for mortality, and if so how mapped?), exposure assignment (station-to-tract methods, lag structure, heat-wave definition), selection of case/control periods and control for seasonality/long-term trends, handling of multiple testing (“note-conditioned hypotheses”), and reporting of effect sizes/uncertainty rather than “fit” improvements. Without a pre-specified analysis plan, clear causal estimand, and reproducible code/data pipeline, the risk of post hoc hypothesis shaping and overinterpretation is high. As written, the conclusion about a “repeatable vulnerability signal” is not yet justified; the more modest conclusion that the pipeline can generate testable hypotheses would be justified if accompanied by transparent methodology and validation.