{"id":"W4386317625","doi":"10.1093/dote/doad052.161","title":"348. IDENTIFYING NOVEL MOLECULAR SUBTYPES OF ESOPHAGEAL ADENOCARCINOMA USING LASER CAPTURE MICRODISSECTED RNA-SEQ SAMPLES","year":2023,"lang":"en","type":"article","venue":"Diseases of the Esophagus","topic":"Esophageal Cancer Research and Treatment","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network","funders":"","keywords":"Laser capture microdissection; Medicine; Microdissection; Esophagus; Gene expression; Biopsy; Pathology; Adenocarcinoma; RNA-Seq; Gene; Gene expression profiling; Immunohistochemistry; Barrett's esophagus; Cohort; Cancer research; Transcriptome; Internal medicine; Cancer; Biology; Genetics","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":[0.0003881222,0.0003837996,0.000331821,0.0006575531,0.0005430736,0.0005608567,0.0002443052,0.0004299687,0.002407932],"category_scores_gemma":[0.0005677192,0.000225261,0.000743699,0.0005186017,0.0002104857,0.000119022,0.0002720708,0.0003435328,0.001508403],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004157162,"about_ca_system_score_gemma":0.0005680697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003564262,"about_ca_topic_score_gemma":0.01127937,"domain_scores_codex":[0.999643,0.00002386896,0.00002539574,0.0001841643,0.00008020169,0.00004343474],"domain_scores_gemma":[0.9997782,0.00007204359,0.00002658228,0.00002546145,0.00007321707,0.00002449029],"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":[0.0004262396,0.00005279287,0.03546415,0.0003996801,0.00009214241,0.0001810776,0.0001665053,0.001862275,0.9309638,0.000274679,0.003896098,0.02622052],"study_design_scores_gemma":[0.0001130282,0.000429422,0.3459837,0.0001127179,0.0003525271,0.001225396,0.0003968054,0.040413,0.5644082,0.001193181,0.04528253,0.0000894297],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8767594,0.001523743,0.04599928,0.0004150388,0.0001388179,0.0003979187,0.06856077,0.001986142,0.004218969],"genre_scores_gemma":[0.712718,0.0009158995,0.143182,0.0007818195,0.00007253229,0.001293157,0.1304897,0.0006868729,0.00986],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003564262,"threshold_uncertainty_score":0.008055329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03946721297128372,"score_gpt":0.3179947893013371,"score_spread":0.2785275763300533,"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."}}