{"id":"W4411375898","doi":"10.1002/mrm.30570","title":"Consensus recommendations for hyperpolarized [1‐ <scp> <sup>13</sup> C </scp> ]pyruvate <scp>MRI</scp> multi‐center human studies","year":2025,"lang":"en","type":"article","venue":"Magnetic Resonance in Medicine","topic":"Advanced NMR Techniques and Applications","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Sunnybrook Health Science Centre","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; National Institutes of Health; Chang Gung Medical Foundation; Eidgenössische Technische Hochschule Zürich; National Institute for Health and Care Research; Aarhus Universitet; Cancer Prevention and Research Institute of Texas","keywords":"Computer science; Calibration; Calibration curve; Medical physics; Quality assurance; Nuclear medicine; Medicine; Nuclear magnetic resonance; Computational biology; Chemistry; Physics; Statistics; Mathematics; Biology; Pathology; Chromatography","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.5221133,0.002279204,0.003823999,0.008448903,0.00674492,0.01077981,0.01933693,0.0186491,0.005388788],"category_scores_gemma":[0.5459975,0.003207123,0.008825584,0.00532788,0.006893978,0.009018089,0.02034609,0.02083865,0.005002126],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01116881,"about_ca_system_score_gemma":0.06787928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00725243,"about_ca_topic_score_gemma":0.005517837,"domain_scores_codex":[0.4787358,0.3584885,0.08249127,0.01440307,0.05520684,0.01067456],"domain_scores_gemma":[0.2414944,0.3532138,0.03784009,0.03229425,0.3097701,0.02538728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001247835,0.001428927,0.009689856,0.04332104,0.001848288,0.002055409,0.04747881,0.006988542,0.008323516,0.03208522,0.3469334,0.4985992],"study_design_scores_gemma":[0.002139032,0.001575916,0.01171932,0.1408059,0.001945441,0.001652124,0.05582163,0.01099265,0.009043795,0.09863798,0.664227,0.001439109],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"methods","genre_scores_codex":[0.02619415,0.03416669,0.2634144,0.5861785,0.03312115,0.02719839,0.002237769,0.002046103,0.02544279],"genre_scores_gemma":[0.1277617,0.01605755,0.7329704,0.06837555,0.003333935,0.0394705,0.003678169,0.000833314,0.007518965],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5221133,"threshold_uncertainty_score":0.5893193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04327034149154413,"score_gpt":0.3598963098351308,"score_spread":0.3166259683435867,"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."}}