{"id":"W2604767602","doi":"10.1039/c7lc00170c","title":"Towards a personalized approach to aromatase inhibitor therapy: a digital microfluidic platform for rapid analysis of estradiol in core-needle-biopsies","year":2017,"lang":"en","type":"article","venue":"Lab on a Chip","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mount Sinai Hospital; University of Toronto; Lunenfeld-Tanenbaum Research Institute; Toronto Public Health","funders":"Canadian Institutes of Health Research","keywords":"Aromatase; Aromatase inhibitor; Core (optical fiber); Core biopsy; Internal medicine; Letrozole; Microfluidics; Personalized medicine; Medicine; Biomedical engineering; Computer science; Nanotechnology; Biology; Bioinformatics; Materials science; Breast cancer; Telecommunications; Cancer","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001604222,0.0002538983,0.0005455301,0.0004697736,0.00008854499,0.0001247164,0.0004177214,0.0001728642,0.000006643554],"category_scores_gemma":[0.0001198178,0.0002093843,0.0002260779,0.0003543985,0.0001582838,0.0001243964,0.00005869793,0.0001326076,0.000004216404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000668429,"about_ca_system_score_gemma":0.00002481946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006196985,"about_ca_topic_score_gemma":0.000004210803,"domain_scores_codex":[0.9989306,0.000004865489,0.0003060127,0.0002867939,0.0001504459,0.0003212667],"domain_scores_gemma":[0.999167,0.0000466401,0.0000805692,0.0006127597,0.00002956474,0.00006344852],"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":[0.001645615,0.0008069217,0.007384777,0.0004619265,0.002393381,0.00002955889,0.00556247,0.0001584843,0.8284191,0.003717755,0.02262008,0.1267999],"study_design_scores_gemma":[0.007809271,0.001060193,0.04323794,0.0004276299,0.0004726039,0.00003135828,0.002700631,0.01380439,0.9026289,0.001109494,0.02497992,0.001737686],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948805,0.001649734,0.00081112,0.0001010537,0.00008861426,0.0004795236,0.0006564697,0.0002422426,0.001090731],"genre_scores_gemma":[0.9976554,0.0004357907,0.001560271,0.00005152623,0.00003904699,0.00004657888,0.0001382234,0.00003055938,0.00004263684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1250622,"threshold_uncertainty_score":0.8538443,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0466742388020923,"score_gpt":0.2575365106548043,"score_spread":0.210862271852712,"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."}}