{"id":"W4386352834","doi":"10.1101/2023.08.30.555487","title":"Using neural biomarkers to personalize dosing of vagus nerve stimulation","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Vagus Nerve Stimulation Research","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"National Institutes of Health; Canada First Research Excellence Fund; Mitacs","keywords":"Vagus nerve stimulation; Dosing; Medicine; Stimulation; Vagus nerve; Neuroscience; Psychology; Pharmacology","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001004727,0.0006191147,0.0006978276,0.001192392,0.0003203427,0.0003260259,0.00097232,0.0004558871,0.00008097377],"category_scores_gemma":[0.002853935,0.0007105661,0.0002940534,0.002364584,0.0002230051,0.0002869219,0.001222621,0.00075279,0.0001074274],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005094446,"about_ca_system_score_gemma":0.0006078386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004000595,"about_ca_topic_score_gemma":0.000003479386,"domain_scores_codex":[0.9945738,0.0005758914,0.0009221715,0.001656748,0.001374368,0.0008969901],"domain_scores_gemma":[0.996352,0.0005460583,0.0006001524,0.001392819,0.0006446198,0.0004643855],"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":[0.00009571459,0.00007064371,0.0072251,0.0003309244,0.00004662692,0.00008506353,0.00002566802,0.07574866,0.9160756,0.0001834542,0.0001094328,0.000003041635],"study_design_scores_gemma":[0.0006166319,0.00007899469,0.08475569,0.0004986443,0.00007923801,4.561914e-8,0.000004893531,0.3007111,0.6123736,0.000005396725,0.00009465434,0.000781132],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9912654,0.0001294986,0.004383487,0.0004106166,0.001460077,0.00142849,0.0003558183,0.0005588707,0.000007729935],"genre_scores_gemma":[0.9928523,0.00001297422,0.006354649,0.0001790771,0.0002801735,0.00007405636,3.620329e-7,0.0002359814,0.00001037366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3037021,"threshold_uncertainty_score":0.9995345,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1225535852485522,"score_gpt":0.3375413990958169,"score_spread":0.2149878138472647,"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."}}