{"id":"W4416114508","doi":"10.1117/1.jbo.30.s2.s23901","title":"Consensus guidelines for cellular label-free optical metabolic imaging: ensuring accuracy and reproducibility in metabolic profiling","year":2025,"lang":"en","type":"article","venue":"Journal of Biomedical Optics","topic":"Advanced Fluorescence Microscopy Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Carleton University","funders":"Leadership Lincoln; National Institute of Arthritis and Musculoskeletal and Skin Diseases; Japan Endocrine Society; National Heart, Lung, and Blood Institute; Perelman School of Medicine, University of Pennsylvania; American Cancer Society; National Cancer Institute; Institute for Translational Medicine and Therapeutics; National Institute of Biomedical Imaging and Bioengineering; University of Pennsylvania; Agence Nationale de la Recherche; National Institutes of Health; National Science Foundation","keywords":"Profiling (computer programming); Reproducibility; Optical imaging; Metabolic activity; Metabolic regulation","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":[0.1334064,0.002234081,0.003194464,0.008268094,0.003741083,0.007784366,0.01752091,0.01705249,0.004085031],"category_scores_gemma":[0.1654198,0.001883129,0.005528418,0.005272093,0.006474523,0.004632156,0.007023322,0.01360897,0.006186329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00686553,"about_ca_system_score_gemma":0.03388208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009839872,"about_ca_topic_score_gemma":0.01038889,"domain_scores_codex":[0.888701,0.04766123,0.02108643,0.004718475,0.03478119,0.00305164],"domain_scores_gemma":[0.6986189,0.09346101,0.01481316,0.02354313,0.1625155,0.007048231],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004913749,0.0006255014,0.003660691,0.02108928,0.0005154016,0.002350478,0.004270677,0.006587516,0.02610233,0.07745259,0.2985089,0.5583453],"study_design_scores_gemma":[0.0001319064,0.0003139511,0.003880231,0.02793941,0.000605816,0.002182327,0.001630599,0.004397382,0.02126604,0.03936833,0.8979604,0.000323507],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009844414,0.123209,0.6110741,0.1540525,0.01576989,0.009586216,0.005043081,0.004456039,0.06696475],"genre_scores_gemma":[0.03298367,0.04499469,0.8554363,0.03030502,0.001914614,0.01396731,0.006099625,0.0009628669,0.013336],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8665936,"threshold_uncertainty_score":0.7055287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02982996736915374,"score_gpt":0.3686084630909869,"score_spread":0.3387784957218332,"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."}}