{"id":"W4411226562","doi":"10.1111/den.15028","title":"Computer‐aided detection for esophageal achalasia (with video)","year":2025,"lang":"en","type":"article","venue":"Digestive Endoscopy","topic":"Gastroesophageal reflux and treatments","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Ipsen; Boston Scientific Corporation","keywords":"Medicine; Achalasia; Radiology; Artificial intelligence; Esophagus; General surgery; Internal medicine; Computer science","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":[0.00006360693,0.0002134797,0.0003150732,0.0001544521,0.0001352556,0.00002462047,0.00006090244,0.0000729219,0.00001586893],"category_scores_gemma":[0.00005513315,0.0001596067,0.00009705852,0.0002188892,0.0000649868,0.00007945374,0.00002400054,0.0001308574,0.00003008778],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001392171,"about_ca_system_score_gemma":0.00009902578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001313308,"about_ca_topic_score_gemma":0.000003608489,"domain_scores_codex":[0.998975,0.00002520082,0.0001727448,0.0003654065,0.0001506972,0.00031098],"domain_scores_gemma":[0.9993088,0.0001571045,0.00006064071,0.000215284,0.0001408044,0.0001174042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003000143,0.0009862081,0.8824624,0.0003503643,0.001269964,0.000921134,0.0003093774,0.0000805125,0.005717457,0.002047108,0.0006567963,0.1021985],"study_design_scores_gemma":[0.009970264,0.003293644,0.860963,0.0002103562,0.0003963574,0.00003659817,0.00002637966,0.0007910309,0.1231882,0.0002900529,0.0007472783,0.00008684635],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8711242,0.0001430495,0.1239882,0.000224471,0.0004145174,0.001387033,0.0000305099,0.0001860142,0.002502067],"genre_scores_gemma":[0.9813432,0.000005256989,0.01615074,0.0003179622,0.0001372578,0.0002223251,0.00006384721,0.00002365939,0.001735803],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1174707,"threshold_uncertainty_score":0.6508571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009953929860288112,"score_gpt":0.2891772208628836,"score_spread":0.2792232910025955,"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."}}