{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:7PFJLTYR7ILRGOA5TJTEZKY3VX","short_pith_number":"pith:7PFJLTYR","schema_version":"1.0","canonical_sha256":"fbca95cf11fa1713381d9a664cab1badc4f0b592e16cdf1fd1979bd50997a959","source":{"kind":"arxiv","id":"2606.29750","version":1},"attestation_state":"computed","paper":{"title":"Managing Map Cardinality in Automatic Disease Classification Mapping: Balancing Precision, Recall and Coverage","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jeewani Anupama Ginige, Jim Basilakis, Oliver Obst, Santosh Purja Pun","submitted_at":"2026-06-29T03:47:35Z","abstract_excerpt":"Automatic mapping between disease classification systems, such as the International Classification of Diseases (ICD), is a challenging yet essential task for integrating health data and conducting longitudinal data analysis. Existing embedding-based methods primarily focus on \\emph{one-to-one} mappings, overlooking more complex \\emph{one-to-many} scenarios. The threshold-based and top-K methods offer natural extensions; however, they involve inherent trade-offs between \\emph{precision}, \\emph{recall} and \\emph{mapping coverage} -- the proportion of source codes with at least one mapping to a t"},"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":"2606.29750","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-06-29T03:47:35Z","cross_cats_sorted":[],"title_canon_sha256":"443ec93ed085c52fe9e1d9bff9c1d9f2f799b1254ad8d1a0b18423a9939c5c90","abstract_canon_sha256":"81c2ca9ac1effc03db722e112e68a705183d04d5fba393f82d80890da80b29e9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:17:33.521692Z","signature_b64":"uIl84ptCWBdjLe3SBFZ0PNpPbrYoeSNpIlzS/wLdqgn5EN8S8/3yH/J+92DLgxsCUa8FzfxErSNbef/zJULLCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbca95cf11fa1713381d9a664cab1badc4f0b592e16cdf1fd1979bd50997a959","last_reissued_at":"2026-06-30T02:17:33.521110Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:17:33.521110Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Managing Map Cardinality in Automatic Disease Classification Mapping: Balancing Precision, Recall and Coverage","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Jeewani Anupama Ginige, Jim Basilakis, Oliver Obst, Santosh Purja Pun","submitted_at":"2026-06-29T03:47:35Z","abstract_excerpt":"Automatic mapping between disease classification systems, such as the International Classification of Diseases (ICD), is a challenging yet essential task for integrating health data and conducting longitudinal data analysis. Existing embedding-based methods primarily focus on \\emph{one-to-one} mappings, overlooking more complex \\emph{one-to-many} scenarios. The threshold-based and top-K methods offer natural extensions; however, they involve inherent trade-offs between \\emph{precision}, \\emph{recall} and \\emph{mapping coverage} -- the proportion of source codes with at least one mapping to a t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.29750","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/2606.29750/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":"2606.29750","created_at":"2026-06-30T02:17:33.521196+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.29750v1","created_at":"2026-06-30T02:17:33.521196+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.29750","created_at":"2026-06-30T02:17:33.521196+00:00"},{"alias_kind":"pith_short_12","alias_value":"7PFJLTYR7ILR","created_at":"2026-06-30T02:17:33.521196+00:00"},{"alias_kind":"pith_short_16","alias_value":"7PFJLTYR7ILRGOA5","created_at":"2026-06-30T02:17:33.521196+00:00"},{"alias_kind":"pith_short_8","alias_value":"7PFJLTYR","created_at":"2026-06-30T02:17:33.521196+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/7PFJLTYR7ILRGOA5TJTEZKY3VX","json":"https://pith.science/pith/7PFJLTYR7ILRGOA5TJTEZKY3VX.json","graph_json":"https://pith.science/api/pith-number/7PFJLTYR7ILRGOA5TJTEZKY3VX/graph.json","events_json":"https://pith.science/api/pith-number/7PFJLTYR7ILRGOA5TJTEZKY3VX/events.json","paper":"https://pith.science/paper/7PFJLTYR"},"agent_actions":{"view_html":"https://pith.science/pith/7PFJLTYR7ILRGOA5TJTEZKY3VX","download_json":"https://pith.science/pith/7PFJLTYR7ILRGOA5TJTEZKY3VX.json","view_paper":"https://pith.science/paper/7PFJLTYR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.29750&json=true","fetch_graph":"https://pith.science/api/pith-number/7PFJLTYR7ILRGOA5TJTEZKY3VX/graph.json","fetch_events":"https://pith.science/api/pith-number/7PFJLTYR7ILRGOA5TJTEZKY3VX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7PFJLTYR7ILRGOA5TJTEZKY3VX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7PFJLTYR7ILRGOA5TJTEZKY3VX/action/storage_attestation","attest_author":"https://pith.science/pith/7PFJLTYR7ILRGOA5TJTEZKY3VX/action/author_attestation","sign_citation":"https://pith.science/pith/7PFJLTYR7ILRGOA5TJTEZKY3VX/action/citation_signature","submit_replication":"https://pith.science/pith/7PFJLTYR7ILRGOA5TJTEZKY3VX/action/replication_record"}},"created_at":"2026-06-30T02:17:33.521196+00:00","updated_at":"2026-06-30T02:17:33.521196+00:00"}