{"id":"W4221054840","doi":"10.2196/32313","title":"Predicting Long-term Survival After Allogeneic Hematopoietic Cell Transplantation in Patients With Hematologic Malignancies: Machine Learning–Based Model Development and Validation","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Hematopoietic Stem Cell Transplantation","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Korea Health Industry Development Institute","keywords":"Hematopoietic cell; Medicine; Transplantation; Receiver operating characteristic; Survival analysis; Machine learning; Hematology; Internal medicine; Oncology; Gradient boosting; Haematopoiesis; Computer science; Random forest; Stem cell","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005603355,0.0002514797,0.0003967231,0.000238539,0.000151674,0.00002385856,0.0001109494,0.0001383358,0.0002082129],"category_scores_gemma":[0.00002280843,0.0002096622,0.00003934717,0.00027316,0.00007214071,0.0001616911,0.00004243372,0.0005105622,0.000007647952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001461157,"about_ca_system_score_gemma":0.0003253938,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003773533,"about_ca_topic_score_gemma":0.00002087902,"domain_scores_codex":[0.9969981,0.0001011724,0.000904254,0.0002014088,0.001446841,0.0003482527],"domain_scores_gemma":[0.9991714,0.0001267609,0.0002363976,0.000158787,0.00007270028,0.0002338887],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006128366,0.0003988393,0.9800619,0.003415896,0.00002262678,0.0001021434,0.01255396,0.002525139,0.00001699139,0.00001170465,0.000001433109,0.0002764924],"study_design_scores_gemma":[0.01776572,0.00100486,0.8738559,0.001546264,0.0002560396,0.0003054331,0.001953749,0.09890023,0.003620907,0.00001573121,0.00003078744,0.0007443147],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9813622,0.00003842236,0.01721496,0.00006694723,0.00004745272,0.0009927417,0.00002165845,0.00008702167,0.0001685672],"genre_scores_gemma":[0.9943899,0.0000211718,0.003839886,0.000313114,0.000007680201,0.0004173878,0.0009477729,0.00002923283,0.00003386597],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.106206,"threshold_uncertainty_score":0.8549778,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01290816897260283,"score_gpt":0.2413543881015175,"score_spread":0.2284462191289146,"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."}}