arXiv:2610.35747v1 Announce Type: new Abstract: Clinical audio models increasingly align recordings with participant metadata or metadata-derived text. This supervision is useful but ambiguous: the paired target can contain symptoms, demographics, missingness, and cohort structure, not just disease information. We study this ambiguity by making the audio-metadata pairing itself the intervention. Our Pairing-Controlled Transfer Audit trains matched contrastive trajectories under correct pairing, global shuffling, disease-label-preserving shuffling, and label-and-recorded-sex-preserving shuffling. Each endpoint is then frozen and tested with three readouts: profile correspondence, attribute decodability, and matched disease prediction. On UKCOVID, correct improves retrieval and sex decodability over label-only shuffling, showing that the pairing intervention worked. The sex-probe contrast is largely absorbed by the sex-preserving control, while smaller retrieval differences remain. Disease-transfer contrasts, however, remain uncertain. After UKCOVID discovery fixed the pipeline, CODA TB, Cambridge COVID-19 Sounds, and Coswara were rerun as a locked external panel with the same arms, backbones, matching, readout, calibration, primary contrast, and primary metric. The external panel preserves the descriptive separation without proving a universal null. Successful profile correspondence and improved disease transfer are therefore different empirical claims.