{"id":"W4411753619","doi":"10.1093/humrep/deaf097.410","title":"P-101 The dual AI system including 3D-AI showed high performance for predicting implantation","year":2025,"lang":"en","type":"article","venue":"Human Reproduction","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Dual (grammatical number); Medicine; Computer science; Art","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":[],"consensus_categories":[],"category_scores_codex":[0.004120568,0.001252778,0.001002116,0.001561869,0.0003754857,0.002387066,0.0006483247,0.001086615,0.004497451],"category_scores_gemma":[0.01106681,0.0003595165,0.001356634,0.000684994,0.000327138,0.001040033,0.001006203,0.0008696475,0.002109364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004228588,"about_ca_system_score_gemma":0.0005309334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001542205,"about_ca_topic_score_gemma":0.001304634,"domain_scores_codex":[0.9979772,0.0006025762,0.0002126225,0.0005892414,0.0004857181,0.0001326282],"domain_scores_gemma":[0.9955479,0.002411774,0.0004551831,0.0002863861,0.001028257,0.0002705395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.004591826,0.0004055624,0.6129948,0.0006878182,0.001737148,0.0005044459,0.0001836946,0.01636012,0.02419196,0.0004612425,0.009877349,0.3280039],"study_design_scores_gemma":[0.0002389267,0.004408472,0.4632013,0.0002627985,0.001930554,0.003855724,0.0003490158,0.4891662,0.02510195,0.001985456,0.009208387,0.0002912204],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9168439,0.006432734,0.05611217,0.0009421441,0.0006216749,0.0002929209,0.004486771,0.002317655,0.01195013],"genre_scores_gemma":[0.9834725,0.0004040131,0.01185311,0.0001153475,0.0001284244,0.00009270561,0.001961306,0.00008373138,0.001888922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004497451,"threshold_uncertainty_score":0.02179193,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1717884218981366,"score_gpt":0.4809703321057862,"score_spread":0.3091819102076496,"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."}}