{"id":"W4409409562","doi":"10.26685/urncst.789","title":"Low-Cost Fine-Tuning of Data-Efficient Image Transformers on Knee X-Ray Imaging for Osteoarthritis Detection","year":2025,"lang":"en","type":"article","venue":"Undergraduate Research in Natural and Clinical Science and Technology (URNCST) Journal","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"University of Toronto","keywords":"Osteoarthritis; Transformer; Computer science; Computer vision; Medicine; Artificial intelligence; Engineering; Electrical engineering; Voltage; 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":"codex-gemma-dda1882f352a","candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.008400328,0.0001511713,0.000417352,0.002208771,0.0006396585,0.00006845353,0.0004277753,0.000172489,0.000004585738],"category_scores_gemma":[0.003297457,0.0001058058,0.00006269079,0.003134592,0.00461422,0.0002642866,0.00020119,0.002373471,0.000001110571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001204415,"about_ca_system_score_gemma":0.0004606561,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001466897,"about_ca_topic_score_gemma":0.00003359378,"domain_scores_codex":[0.9971303,0.0001186961,0.0007059906,0.0005484472,0.0007827422,0.000713856],"domain_scores_gemma":[0.9976073,0.000933212,0.0001267508,0.0003063576,0.0008203864,0.0002060046],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005955628,0.0001996157,0.006461269,0.0000944252,0.00003471056,0.00007961306,0.0000239181,0.000001763888,0.04558336,0.001238868,0.0001378317,0.9455491],"study_design_scores_gemma":[0.102204,0.03475196,0.1397956,0.0266687,0.0008468128,0.004953106,0.01765983,0.2174023,0.1286999,0.3079588,0.01670876,0.002350178],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9392069,0.002030465,0.001983157,0.05452083,0.0004809553,0.0009928741,0.00001288435,0.00003726251,0.0007346537],"genre_scores_gemma":[0.9969745,0.001666686,0.001045396,0.0001603774,0.00007630112,0.00001527852,0.000004050732,0.000007642121,0.0000497276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9431989,"threshold_uncertainty_score":0.9999281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04505475350409808,"score_gpt":0.4218199750272821,"score_spread":0.376765221523184,"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."}}