{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZFFXWRMXFRVPUN3BCFTWMWY6A7","short_pith_number":"pith:ZFFXWRMX","schema_version":"1.0","canonical_sha256":"c94b7b45972c6afa37611167665b1e07de4253bbc62da0b0b44de274b2293e2a","source":{"kind":"arxiv","id":"2411.14795","version":1},"attestation_state":"computed","paper":{"title":"De-biased Multimodal Electrocardiogram Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Haitao Li, Yiheng Mao, Zhengxing Huang, Zhoujian Sun, Ziyi Liu, Ziyu Li","submitted_at":"2024-11-22T08:35:35Z","abstract_excerpt":"Multimodal large language models (MLLMs) are increasingly being applied in the medical field, particularly in medical imaging. However, developing MLLMs for ECG signals, which are crucial in clinical settings, has been a significant challenge beyond medical imaging. Previous studies have attempted to address this by converting ECGs into several text tags using an external classifier in a training-free manner. However, this approach significantly compresses the information in ECGs and underutilizes the reasoning capabilities of LLMs. In this work, we directly feed the embeddings of ECGs into th"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2411.14795","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-22T08:35:35Z","cross_cats_sorted":[],"title_canon_sha256":"eec5b450b9cece24305744ea03d9fe9ba4a19d6bfee6e5d96b9df68969a9bfa2","abstract_canon_sha256":"a3efb6d80f63ed528865786b2becba977d309aef18eb049726048e594d8c3375"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:39:12.040405Z","signature_b64":"vn3jnGO66uQ75gGKa5fpLzO75bZmtOMkhpeqEALQuAdiSraAiCs6A354zWb8UdFnC3rSxvcHqYn2KwgPOfP1Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c94b7b45972c6afa37611167665b1e07de4253bbc62da0b0b44de274b2293e2a","last_reissued_at":"2026-07-05T09:39:12.039942Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:39:12.039942Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"De-biased Multimodal Electrocardiogram Analysis","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Haitao Li, Yiheng Mao, Zhengxing Huang, Zhoujian Sun, Ziyi Liu, Ziyu Li","submitted_at":"2024-11-22T08:35:35Z","abstract_excerpt":"Multimodal large language models (MLLMs) are increasingly being applied in the medical field, particularly in medical imaging. However, developing MLLMs for ECG signals, which are crucial in clinical settings, has been a significant challenge beyond medical imaging. Previous studies have attempted to address this by converting ECGs into several text tags using an external classifier in a training-free manner. However, this approach significantly compresses the information in ECGs and underutilizes the reasoning capabilities of LLMs. In this work, we directly feed the embeddings of ECGs into th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.14795","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.14795/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2411.14795","created_at":"2026-07-05T09:39:12.040008+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.14795v1","created_at":"2026-07-05T09:39:12.040008+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.14795","created_at":"2026-07-05T09:39:12.040008+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZFFXWRMXFRVP","created_at":"2026-07-05T09:39:12.040008+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZFFXWRMXFRVPUN3B","created_at":"2026-07-05T09:39:12.040008+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZFFXWRMX","created_at":"2026-07-05T09:39:12.040008+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZFFXWRMXFRVPUN3BCFTWMWY6A7","json":"https://pith.science/pith/ZFFXWRMXFRVPUN3BCFTWMWY6A7.json","graph_json":"https://pith.science/api/pith-number/ZFFXWRMXFRVPUN3BCFTWMWY6A7/graph.json","events_json":"https://pith.science/api/pith-number/ZFFXWRMXFRVPUN3BCFTWMWY6A7/events.json","paper":"https://pith.science/paper/ZFFXWRMX"},"agent_actions":{"view_html":"https://pith.science/pith/ZFFXWRMXFRVPUN3BCFTWMWY6A7","download_json":"https://pith.science/pith/ZFFXWRMXFRVPUN3BCFTWMWY6A7.json","view_paper":"https://pith.science/paper/ZFFXWRMX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.14795&json=true","fetch_graph":"https://pith.science/api/pith-number/ZFFXWRMXFRVPUN3BCFTWMWY6A7/graph.json","fetch_events":"https://pith.science/api/pith-number/ZFFXWRMXFRVPUN3BCFTWMWY6A7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZFFXWRMXFRVPUN3BCFTWMWY6A7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZFFXWRMXFRVPUN3BCFTWMWY6A7/action/storage_attestation","attest_author":"https://pith.science/pith/ZFFXWRMXFRVPUN3BCFTWMWY6A7/action/author_attestation","sign_citation":"https://pith.science/pith/ZFFXWRMXFRVPUN3BCFTWMWY6A7/action/citation_signature","submit_replication":"https://pith.science/pith/ZFFXWRMXFRVPUN3BCFTWMWY6A7/action/replication_record"}},"created_at":"2026-07-05T09:39:12.040008+00:00","updated_at":"2026-07-05T09:39:12.040008+00:00"}