{"id":"W4414458916","doi":"10.1109/tbme.2025.3613757","title":"Next-Generation Tactile Sensing and Machine Learning Integration for Robot-Assisted Minimally Invasive Surgery","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Biomedical Engineering","topic":"Soft Robotics and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"London Health Sciences Centre","funders":"","keywords":"Tactile sensor; Feature extraction; Wearable computer; Process (computing); Haptic technology; Data acquisition; Artificial neural network; Palpation","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001188878,0.0001776622,0.0001995139,0.0003502834,0.000160759,0.00007648968,0.00004696865,0.0001373024,0.000008040493],"category_scores_gemma":[0.00005530466,0.0001892596,0.00008270621,0.0003968959,0.00002847299,0.0001072732,0.000001107617,0.0002643747,0.000002647786],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009006125,"about_ca_system_score_gemma":0.00003457683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001947498,"about_ca_topic_score_gemma":0.0000376204,"domain_scores_codex":[0.9991887,0.00001067207,0.0002837569,0.0002059227,0.0001067528,0.000204219],"domain_scores_gemma":[0.9991934,0.0005318029,0.00002327083,0.0001115881,0.00004476521,0.00009515189],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007058277,0.00003271088,0.00000216529,0.00009779481,0.000071095,0.000001220707,0.00005708768,0.3529735,0.4708236,0.00004797473,0.0002067087,0.175679],"study_design_scores_gemma":[0.0002074701,0.00002386398,0.00008835776,0.0001108041,0.0000441206,0.000006019036,0.00002754189,0.9144489,0.08297379,0.000009674896,0.001898116,0.0001613542],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02819326,0.0001282706,0.9700645,0.0002712467,0.0006955316,0.0002185662,0.00001878597,0.0003836001,0.00002627287],"genre_scores_gemma":[0.985474,0.0002024044,0.01394989,0.00004216388,0.00008515614,0.00007333962,0.00006804753,0.00003488522,0.0000701081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9572808,"threshold_uncertainty_score":0.7717785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03043260566945229,"score_gpt":0.2291416574254808,"score_spread":0.1987090517560285,"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."}}