{"id":"W4387207201","doi":"10.1016/j.ijrobp.2023.06.2124","title":"Low Tesla MR Imaging for Spine with Hardware","year":2023,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Medical Imaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"","keywords":"Medicine; SPINE (molecular biology); Nuclear medicine; Radiology; Medical physics; Computer hardware","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":[0.0008023652,0.0005662029,0.0002987598,0.000879106,0.000670747,0.002033758,0.001142777,0.001167072,0.02412805],"category_scores_gemma":[0.00346591,0.0006719826,0.0003107666,0.0007447355,0.0003870263,0.001479261,0.0009321043,0.001042473,0.005270288],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004872093,"about_ca_system_score_gemma":0.001156536,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001416882,"about_ca_topic_score_gemma":0.003060207,"domain_scores_codex":[0.9997098,0.00006966862,0.00004922613,0.00005850717,0.00007392284,0.00003886439],"domain_scores_gemma":[0.9984391,0.0005998303,0.000120594,0.0003939619,0.0003600705,0.00008642911],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00195793,0.0001757255,0.01118388,0.003767159,0.0003167505,0.002021623,0.0004984108,0.004564736,0.6047518,0.01719161,0.05429074,0.2992795],"study_design_scores_gemma":[0.000332928,0.002090598,0.02950348,0.001478352,0.0009245111,0.02954814,0.0005917652,0.03752062,0.5374872,0.01866218,0.3415315,0.0003286581],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1389374,0.01252539,0.7658893,0.008013708,0.001045465,0.0006182172,0.003129656,0.01542755,0.05441332],"genre_scores_gemma":[0.4063973,0.003000819,0.5628586,0.003335851,0.0002987457,0.0003063964,0.002021959,0.002696142,0.01908415],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02412805,"threshold_uncertainty_score":0.08071637,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02389692610204754,"score_gpt":0.3662089722475365,"score_spread":0.3423120461454889,"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."}}