{"id":"W6982373777","doi":"","title":"Improving treatment outcomes through personalised medicine - assessment of disease activity in acromegaly.","year":2022,"lang":"en","type":"dissertation","venue":"CERES (Cranfield University)","topic":"Pituitary Gland Disorders and Treatments","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Disease; Concordance; Acromegaly; Precision medicine; MEDLINE; Health care; Disease management; Alternative medicine; Best practice","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01033539,0.0001560115,0.0001692212,0.0005112121,0.000702312,0.001724162,0.0004463777,0.0003344249,0.00324429],"category_scores_gemma":[0.01483504,0.00009101834,0.000402733,0.0005545936,0.001159018,0.001270589,0.002147454,0.00120161,0.0003985198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002071413,"about_ca_system_score_gemma":0.003250064,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002590679,"about_ca_topic_score_gemma":0.006225952,"domain_scores_codex":[0.9931359,0.005240205,0.0003510129,0.0001879029,0.0008186646,0.0002662742],"domain_scores_gemma":[0.9911725,0.004345687,0.002334394,0.0005190108,0.0007058386,0.0009224748],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002533619,0.001015903,0.1416667,0.002086347,0.00009056498,0.0003102495,0.0593521,0.001098366,0.001633405,0.006420164,0.024355,0.761718],"study_design_scores_gemma":[0.0003567136,0.003697059,0.5925252,0.008700689,0.0002621584,0.002351421,0.09999711,0.007600334,0.006613337,0.02365605,0.2538476,0.0003922514],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8476313,0.006132645,0.02122075,0.05239035,0.0004169323,0.001700617,0.001293747,0.0004078923,0.06880584],"genre_scores_gemma":[0.9690937,0.002309011,0.02310263,0.002356431,0.00008109475,0.0004488271,0.0003176257,0.00002103539,0.002269585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01033539,"threshold_uncertainty_score":0.05465943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02178913430754796,"score_gpt":0.308451221326513,"score_spread":0.2866620870189651,"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."}}