{"id":"W4386221199","doi":"10.1136/archdischild-2023-325960","title":"Mobile apps and children’s privacy: a traffic analysis of data sharing practices among children’s mobile iOS apps","year":2023,"lang":"en","type":"letter","venue":"Archives of Disease in Childhood","topic":"Child Development and Digital Technology","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institute for Clinical Evaluative Sciences; SickKids Foundation; Hospital for Sick Children; University of Toronto","funders":"Government of Canada","keywords":"Medicine; Mobile apps; Internet privacy; Data sharing; World Wide Web; Patient privacy; Alternative medicine; Pathology; Computer science; Health care","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004676718,0.0005167615,0.001195156,0.002223921,0.0002299276,0.0001566983,0.003370298,0.0004238343,0.00004943266],"category_scores_gemma":[0.001035664,0.0005352261,0.0003781693,0.001898996,0.001271908,0.0007449123,0.002334754,0.001114736,0.000006861352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003956335,"about_ca_system_score_gemma":0.0004809112,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001161476,"about_ca_topic_score_gemma":0.001366778,"domain_scores_codex":[0.9954126,0.000235242,0.001052158,0.001703555,0.0008382692,0.0007581778],"domain_scores_gemma":[0.9953463,0.001023207,0.001446804,0.00190967,0.00003588047,0.0002381375],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008858546,0.00105747,0.9209746,0.0002329258,0.004053935,0.00006417415,0.01983992,0.0005074835,0.000001713138,0.0001687007,0.01434765,0.03866287],"study_design_scores_gemma":[0.0007449554,0.00009640813,0.9885867,0.0005218497,0.001702709,0.000001185088,0.00067012,0.0003992142,0.000003819126,0.001610488,0.004932025,0.000730524],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9842748,0.002290102,0.000006439702,0.007407118,0.00008607242,0.002135062,0.002422545,0.000263939,0.001113933],"genre_scores_gemma":[0.9864442,0.00383169,0.0003387127,0.001876768,0.0004512034,0.0002575981,0.006497805,0.00008767673,0.0002142961],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06761213,"threshold_uncertainty_score":0.9997099,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03026333610714217,"score_gpt":0.3110999374330444,"score_spread":0.2808366013259022,"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."}}