{"id":"W4206975023","doi":"10.32920/16834363","title":"On The Development Of Photoacoustic Imaging Biomarkers For Cancer Treatment Monitoring","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"Terry Fox Foundation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Photoacoustic imaging in biomedicine; Oxygenation; Biomarker; Imaging biomarker; Medicine; Biomedical engineering; Cancer; Cancer research; Radiology; Computer science; Internal medicine; Chemistry; Magnetic resonance imaging; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00108418,0.0004244595,0.0004149173,0.000555923,0.0002259948,0.001445634,0.0005718676,0.001039233,0.002100396],"category_scores_gemma":[0.001402924,0.0003359705,0.0004444149,0.0004643172,0.0007186051,0.001506243,0.0006489927,0.001292638,0.001187458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006155663,"about_ca_system_score_gemma":0.0005393418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000442338,"about_ca_topic_score_gemma":0.0003928025,"domain_scores_codex":[0.9995062,0.00009689771,0.00001795377,0.00008618007,0.0002577675,0.00003495211],"domain_scores_gemma":[0.9992434,0.0003191083,0.0001259932,0.00005898656,0.0002192539,0.00003326993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001303485,0.0001962154,0.00164479,0.001151612,0.00005530448,0.0002150281,0.0003128143,0.02361381,0.6701219,0.05053196,0.005087689,0.2469386],"study_design_scores_gemma":[0.00002985596,0.0008921237,0.001898586,0.0005473603,0.00006845655,0.0005496095,0.0001555374,0.1198989,0.7228158,0.01163867,0.1413842,0.0001209857],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1039072,0.09112789,0.7406626,0.01153276,0.001443081,0.0006086155,0.0003198665,0.001073527,0.04932454],"genre_scores_gemma":[0.4412742,0.09525124,0.4205753,0.003886372,0.0005548608,0.0006781959,0.0003882821,0.0002433687,0.03714821],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002100396,"threshold_uncertainty_score":0.007026553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02582303871276779,"score_gpt":0.2689154035709631,"score_spread":0.2430923648581953,"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."}}