{"schema_version":"secwatch.filing_event.v1","accession":"0001171843-24-003176","form_type":"8-K","ticker":"AGPU","cik":"0001446159","company_name":"Axe Compute Inc.","filed_at":"2024-05-30T23:59:59+00:00","discovered_at":"2026-05-14T18:03:18.455162+00:00","generated_at":"2026-06-01T07:22:28.491158+00:00","sec_items":["8.01","9.01"],"event_type":"other_material","sentiment":"positive","materiality_score":0.65,"calibrated_materiality_score":0.65,"confidence":"high","headline":"Predictive Oncology ovarian cancer ML study results positive; to be presented at ASCO 2024","bullets":["Retrospective study with UPMC Magee-Womens Hospital used 160 multi-omic ML models to predict ovarian cancer survival.","Seven models achieved high accuracy at two-year threshold; 13 at five-year, outperforming clinical data alone.","Analysis used data from 2010-2016 including WES, WTS, drug response, and digital pathology profiles.","Top performing models identified different drivers for short- vs long-term survival, enabling future clinical decision tools.","Results presented at ASCO Annual Meeting in Chicago, June 3, 2024."],"urls":{"canonical":"https://secwatch.observer/filing/0001171843-24-003176","json":"https://secwatch.observer/filing/0001171843-24-003176.json","markdown":"https://secwatch.observer/filing/0001171843-24-003176.md","text":"https://secwatch.observer/filing/0001171843-24-003176.txt","edgar_index":"https://www.sec.gov/Archives/edgar/data/1446159/000117184324003176/0001171843-24-003176-index.htm","edgar_primary_document":"https://www.sec.gov/Archives/edgar/data/1446159/000117184324003176/f8k_053024.htm"},"model":{"generated_by":"deepseek-v4-flash:cloud@v2","generated_at":"2026-06-01T07:22:28.491158+00:00"},"review":{"review_status":"machine_generated","human_reviewed":false,"corrected":false,"correction_note":null,"correction_timestamp":null,"superseded_by":null,"related_filings":[]},"source_grounded_claims":[],"license":"Source filings: public domain (SEC EDGAR). Summaries (headline + bullets): CC-BY-4.0; attribute https://secwatch.observer"}