{"id":"W4289721757","doi":"10.29026/oea.2022.210130","title":"Microchip imaging cytometer: making healthcare available, accessible, and affordable","year":2022,"lang":"en","type":"article","venue":"Opto-Electronic Advances","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Software portability; Computer science; Microfluidics; Point of care; Sample (material); Adaptability; Point-of-care testing; Health care; Systems engineering; Embedded system; Nanotechnology; Computer hardware; Engineering; Medicine; Materials science; Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002211508,0.001103916,0.001187918,0.002421403,0.0006226778,0.002183812,0.002350152,0.001882445,0.005240425],"category_scores_gemma":[0.00283677,0.000629111,0.0005418373,0.001442775,0.0006897986,0.001325702,0.001789939,0.002454078,0.005844844],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009584078,"about_ca_system_score_gemma":0.001775704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006785955,"about_ca_topic_score_gemma":0.001103845,"domain_scores_codex":[0.9967185,0.0003780409,0.0001899802,0.0005178351,0.002052101,0.0001435294],"domain_scores_gemma":[0.9982281,0.0004487025,0.0001823497,0.0001452709,0.0008347068,0.0001609253],"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":[0.0004610965,0.0002043843,0.002671723,0.001874619,0.0001145858,0.000283615,0.000198464,0.001007966,0.4763174,0.0166199,0.09934369,0.4009025],"study_design_scores_gemma":[0.00008386513,0.0002796451,0.002803021,0.000222305,0.0001539585,0.002110501,0.00006281705,0.01418644,0.4483309,0.002668182,0.528892,0.000206377],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02579802,0.06504493,0.8217756,0.008289461,0.006365441,0.001903656,0.005141323,0.0265426,0.03913901],"genre_scores_gemma":[0.04777817,0.02378912,0.8899825,0.00438638,0.001852582,0.00190478,0.003767853,0.001072953,0.02546577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005240425,"threshold_uncertainty_score":0.01753092,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006475024960840035,"score_gpt":0.2258948521920258,"score_spread":0.2194198272311857,"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."}}