{"id":"W4379743990","doi":"10.21203/rs.3.rs-3027617/v1","title":"From Voxels to Prognosis: AI-Driven Quantitative Chest CT Analysis Forecasts ICU Requirements in 81 COVID-19 Cases","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"National Institutes of Health","keywords":"Coronavirus disease 2019 (COVID-19); Medicine; Intensive care unit; Lung; Radiological weapon; Parenchyma; Lobe; Radiology; Voxel; Computed tomography; Internal medicine; Pathology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00160115,0.0005543639,0.0004032128,0.001508747,0.0002282204,0.001304899,0.0006929791,0.001011978,0.00211063],"category_scores_gemma":[0.01182844,0.000340344,0.0005116301,0.0007677235,0.0003277374,0.0006214245,0.0006641647,0.0006601966,0.0008071047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000713005,"about_ca_system_score_gemma":0.000542777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01013342,"about_ca_topic_score_gemma":0.006412717,"domain_scores_codex":[0.9993069,0.0002236594,0.00004805011,0.0001741033,0.0001156948,0.0001316481],"domain_scores_gemma":[0.9953062,0.00290722,0.0005773696,0.0002501259,0.0005462824,0.0004128081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009472668,0.0001521207,0.9214651,0.00005802513,0.00008901077,0.0006608625,0.0001453314,0.04440876,0.001572037,0.0006253502,0.007066717,0.02280949],"study_design_scores_gemma":[0.0000521957,0.0001466613,0.3913716,0.00005397721,0.00007878257,0.0008126477,0.000712,0.5999606,0.002129327,0.002929896,0.00170521,0.00004710886],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9898821,0.0002297847,0.004403288,0.0006526614,0.00003767357,0.00002749851,0.003769838,0.0001249344,0.0008722778],"genre_scores_gemma":[0.9942931,0.00005061998,0.001852116,0.0000328416,0.00002954663,0.000008656078,0.003472804,0.00001796952,0.0002423569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01013342,"threshold_uncertainty_score":0.02014887,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3954685622394646,"score_gpt":0.5529007057984786,"score_spread":0.157432143559014,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}