{"id":"W3089205008","doi":"10.1038/s41598-020-74160-3","title":"Improving maximal safe brain tumor resection with photoacoustic remote sensing microscopy","year":2020,"lang":"en","type":"preprint","venue":"Scientific Reports","topic":"Photoacoustic and Ultrasonic Imaging","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Waterloo","funders":"Centre for Bioengineering and Biotechnology, University of Waterloo; University of Waterloo; Natural Sciences and Engineering Research Council of Canada; Mitacs; illumiSonics","keywords":"H&E stain; Medicine; Photoacoustic imaging in biomedicine; Resection; Brain tumor; Pathology; Radiology; Biomedical engineering; Staining; Surgery; Optics; Physics","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":[0.0002998246,0.0005798429,0.0002554505,0.0003276352,0.0001539267,0.0004243279,0.0005003122,0.0006307338,0.001458335],"category_scores_gemma":[0.0004913156,0.0003119031,0.0002778018,0.0001397466,0.0003503937,0.0006974091,0.0007703002,0.0004906501,0.001010686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002038417,"about_ca_system_score_gemma":0.0003358889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002878809,"about_ca_topic_score_gemma":0.0005350571,"domain_scores_codex":[0.9997352,0.00003162416,0.00001176509,0.00007204235,0.0001125763,0.00003676459],"domain_scores_gemma":[0.9997297,0.00009704646,0.00006861729,0.00004165339,0.00004618082,0.00001681066],"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":[0.00003293671,0.00001224541,0.0001030683,0.00006708049,0.000003734544,0.00005356576,0.00002531665,0.0007960041,0.9877702,0.0003159664,0.0001794417,0.01064033],"study_design_scores_gemma":[0.000009225739,0.0001816805,0.0008580699,0.000009531095,0.00001132925,0.0003999306,0.00003813024,0.02127348,0.9737604,0.0004158505,0.003019277,0.00002317692],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3906569,0.002552114,0.5949657,0.0006410572,0.000142998,0.0002040485,0.0001977162,0.003638994,0.007000424],"genre_scores_gemma":[0.6251151,0.001480979,0.3690959,0.0001890049,0.00007395005,0.0001570649,0.0001745554,0.000177082,0.003536379],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001458335,"threshold_uncertainty_score":0.00487864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009795861604719121,"score_gpt":0.2256271564605974,"score_spread":0.2158312948558783,"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."}}