{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:O5AMJX54CBPBLVE6MAYBMNKI7X","short_pith_number":"pith:O5AMJX54","canonical_record":{"source":{"id":"2504.12494","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-16T21:24:38Z","cross_cats_sorted":[],"title_canon_sha256":"b26d7802144873f5ed9671c3b3822f5237632eb9b18b033dd0e967c589ac20a7","abstract_canon_sha256":"d1651e4904a9e46c15fae01401f6bc82598ea7e3027ed3a2992897402621f22d"},"schema_version":"1.0"},"canonical_sha256":"7740c4dfbc105e15d49e6030163548fde3df13ce9e33dfbb667edb04209754d2","source":{"kind":"arxiv","id":"2504.12494","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.12494","created_at":"2026-07-05T10:50:17Z"},{"alias_kind":"arxiv_version","alias_value":"2504.12494v1","created_at":"2026-07-05T10:50:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12494","created_at":"2026-07-05T10:50:17Z"},{"alias_kind":"pith_short_12","alias_value":"O5AMJX54CBPB","created_at":"2026-07-05T10:50:17Z"},{"alias_kind":"pith_short_16","alias_value":"O5AMJX54CBPBLVE6","created_at":"2026-07-05T10:50:17Z"},{"alias_kind":"pith_short_8","alias_value":"O5AMJX54","created_at":"2026-07-05T10:50:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:O5AMJX54CBPBLVE6MAYBMNKI7X","target":"record","payload":{"canonical_record":{"source":{"id":"2504.12494","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-16T21:24:38Z","cross_cats_sorted":[],"title_canon_sha256":"b26d7802144873f5ed9671c3b3822f5237632eb9b18b033dd0e967c589ac20a7","abstract_canon_sha256":"d1651e4904a9e46c15fae01401f6bc82598ea7e3027ed3a2992897402621f22d"},"schema_version":"1.0"},"canonical_sha256":"7740c4dfbc105e15d49e6030163548fde3df13ce9e33dfbb667edb04209754d2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:17.226415Z","signature_b64":"EdOTcHlfdw2clskKGPX8qZz4a5tWHjDaYwx9EZrTY5mU7pIruZmgCRPena64sm9gTw8OxvuutXTv7CT3hfd5AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7740c4dfbc105e15d49e6030163548fde3df13ce9e33dfbb667edb04209754d2","last_reissued_at":"2026-07-05T10:50:17.225928Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:17.225928Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.12494","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:50:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DCIA4dJo1096r90tdKmFvMsTTAIz120mIyedoR5/B3JmrJS6Lu1dYYmVB6Ckd94l7UmfIe07FCkR8LgQjGkPBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:52:45.934591Z"},"content_sha256":"fb142fc5e81df709ef0d25a1cfe15b93900ea79f575df52b7bd3e70dee55215f","schema_version":"1.0","event_id":"sha256:fb142fc5e81df709ef0d25a1cfe15b93900ea79f575df52b7bd3e70dee55215f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:O5AMJX54CBPBLVE6MAYBMNKI7X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerating Clinical NLP at Scale with a Hybrid Framework with Reduced GPU Demands: A Case Study in Dementia Identification","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Adam P. Bress, Annie Bowles, Elizabeth Hanchrow, Jianlin Shi, John Stanley, Jordana B. Cohen, Patrick R. Alba, Qiwei Gan","submitted_at":"2025-04-16T21:24:38Z","abstract_excerpt":"Clinical natural language processing (NLP) is increasingly in demand in both clinical research and operational practice. However, most of the state-of-the-art solutions are transformers-based and require high computational resources, limiting their accessibility. We propose a hybrid NLP framework that integrates rule-based filtering, a Support Vector Machine (SVM) classifier, and a BERT-based model to improve efficiency while maintaining accuracy. We applied this framework in a dementia identification case study involving 4.9 million veterans with incident hypertension, analyzing 2.1 billion c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12494","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/2504.12494/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:50:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k1oavTU9dpR9sf4Q8xDfApEat+FhmtVaQAiP6Q30b3tlzk2nqSSua5gQH1gjCrAvIMJkMibf1y1rQ2w3V9LVAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T13:52:45.934962Z"},"content_sha256":"c58bd051eb0cc98bd1e852dcfa0baab32eb757028ab319ba819341f88bc86d38","schema_version":"1.0","event_id":"sha256:c58bd051eb0cc98bd1e852dcfa0baab32eb757028ab319ba819341f88bc86d38"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O5AMJX54CBPBLVE6MAYBMNKI7X/bundle.json","state_url":"https://pith.science/pith/O5AMJX54CBPBLVE6MAYBMNKI7X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O5AMJX54CBPBLVE6MAYBMNKI7X/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T13:52:45Z","links":{"resolver":"https://pith.science/pith/O5AMJX54CBPBLVE6MAYBMNKI7X","bundle":"https://pith.science/pith/O5AMJX54CBPBLVE6MAYBMNKI7X/bundle.json","state":"https://pith.science/pith/O5AMJX54CBPBLVE6MAYBMNKI7X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O5AMJX54CBPBLVE6MAYBMNKI7X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:O5AMJX54CBPBLVE6MAYBMNKI7X","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d1651e4904a9e46c15fae01401f6bc82598ea7e3027ed3a2992897402621f22d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-16T21:24:38Z","title_canon_sha256":"b26d7802144873f5ed9671c3b3822f5237632eb9b18b033dd0e967c589ac20a7"},"schema_version":"1.0","source":{"id":"2504.12494","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.12494","created_at":"2026-07-05T10:50:17Z"},{"alias_kind":"arxiv_version","alias_value":"2504.12494v1","created_at":"2026-07-05T10:50:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12494","created_at":"2026-07-05T10:50:17Z"},{"alias_kind":"pith_short_12","alias_value":"O5AMJX54CBPB","created_at":"2026-07-05T10:50:17Z"},{"alias_kind":"pith_short_16","alias_value":"O5AMJX54CBPBLVE6","created_at":"2026-07-05T10:50:17Z"},{"alias_kind":"pith_short_8","alias_value":"O5AMJX54","created_at":"2026-07-05T10:50:17Z"}],"graph_snapshots":[{"event_id":"sha256:c58bd051eb0cc98bd1e852dcfa0baab32eb757028ab319ba819341f88bc86d38","target":"graph","created_at":"2026-07-05T10:50:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2504.12494/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Clinical natural language processing (NLP) is increasingly in demand in both clinical research and operational practice. However, most of the state-of-the-art solutions are transformers-based and require high computational resources, limiting their accessibility. We propose a hybrid NLP framework that integrates rule-based filtering, a Support Vector Machine (SVM) classifier, and a BERT-based model to improve efficiency while maintaining accuracy. We applied this framework in a dementia identification case study involving 4.9 million veterans with incident hypertension, analyzing 2.1 billion c","authors_text":"Adam P. Bress, Annie Bowles, Elizabeth Hanchrow, Jianlin Shi, John Stanley, Jordana B. Cohen, Patrick R. Alba, Qiwei Gan","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-16T21:24:38Z","title":"Accelerating Clinical NLP at Scale with a Hybrid Framework with Reduced GPU Demands: A Case Study in Dementia Identification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12494","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:fb142fc5e81df709ef0d25a1cfe15b93900ea79f575df52b7bd3e70dee55215f","target":"record","created_at":"2026-07-05T10:50:17Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d1651e4904a9e46c15fae01401f6bc82598ea7e3027ed3a2992897402621f22d","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-04-16T21:24:38Z","title_canon_sha256":"b26d7802144873f5ed9671c3b3822f5237632eb9b18b033dd0e967c589ac20a7"},"schema_version":"1.0","source":{"id":"2504.12494","kind":"arxiv","version":1}},"canonical_sha256":"7740c4dfbc105e15d49e6030163548fde3df13ce9e33dfbb667edb04209754d2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7740c4dfbc105e15d49e6030163548fde3df13ce9e33dfbb667edb04209754d2","first_computed_at":"2026-07-05T10:50:17.225928Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:50:17.225928Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EdOTcHlfdw2clskKGPX8qZz4a5tWHjDaYwx9EZrTY5mU7pIruZmgCRPena64sm9gTw8OxvuutXTv7CT3hfd5AA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:50:17.226415Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.12494","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fb142fc5e81df709ef0d25a1cfe15b93900ea79f575df52b7bd3e70dee55215f","sha256:c58bd051eb0cc98bd1e852dcfa0baab32eb757028ab319ba819341f88bc86d38"],"state_sha256":"44417ce681127955d83b08978ec644b6efe8c71c0c5af0c0790ecb2c28fc2ad3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/xTuuBPmPtTDzoVu0IZjj3LjVtu7d4F0BCyyq4P9zg7HwOaEsWWdXQ7FqrIaAdtXH5A1huZMVDB28GCvuDctDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T13:52:45.937263Z","bundle_sha256":"c5cc5a32d65377d28caed2564788ef3f384b5b8c20aa6b35710f518e31d646c4"}}