{"id":"W2219473791","doi":"10.22329/wyaj.v28i1.4493","title":"Human Rights Disclosure Litigation: Uncovering Invisible Medical Records","year":2010,"lang":"en","type":"article","venue":"Windsor Yearbook of Access to Justice","topic":"Patient Dignity and Privacy","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Human rights; Confidentiality; Neglect; Relevance (law); Jurisprudence; Internet privacy; Business; Vulnerability (computing); Political science; Medical record; Personally identifiable information; Law; Psychology; Medicine; Computer security; Psychiatry; Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003181174,0.0001492008,0.0002948128,0.0001655058,0.0001481497,0.00004738425,0.0004357535,0.000196007,0.002062895],"category_scores_gemma":[0.0005295401,0.0001303866,0.00007830543,0.0002844395,0.0001047186,0.0003556834,0.0002180065,0.0005102619,0.000113289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002272141,"about_ca_system_score_gemma":0.0001375363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002876271,"about_ca_topic_score_gemma":0.0003181942,"domain_scores_codex":[0.9984161,0.00003278836,0.0003710659,0.0002677883,0.0006656416,0.0002466617],"domain_scores_gemma":[0.9987502,0.0001099551,0.0001130733,0.0004615219,0.0001596691,0.0004056456],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002007518,0.004235134,0.6595871,0.009062577,0.00099298,0.0009200118,0.008593486,0.0002042887,0.1455673,0.04010283,0.1050203,0.02370645],"study_design_scores_gemma":[0.0049413,0.001448355,0.7036047,0.002228478,0.001133759,0.00009446929,0.0001630742,0.0002250095,0.0840223,0.008164711,0.1930209,0.0009529093],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.938457,0.00001740822,0.0001091983,0.00150106,0.0006020472,0.0003123235,0.000011304,0.00006155439,0.05892817],"genre_scores_gemma":[0.9958947,0.000002336395,0.0009455194,0.0008844666,0.0009498515,0.00001431851,0.0000224918,0.0000205576,0.001265764],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08800064,"threshold_uncertainty_score":0.9988493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04647661731335942,"score_gpt":0.3538727867256669,"score_spread":0.3073961694123075,"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."}}