{"id":"W4398142733","doi":"10.2196/52577","title":"“Notification! You May Have Cancer.” Could Smartphones and Wearables Help Detect Cancer Early?","year":2024,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Medical Research Council; Cancer Research UK; Barts Charity","keywords":"Wearable computer; Context (archaeology); Wearable technology; Health care; Computer science; Risk analysis (engineering); Unintended consequences; Internet privacy; Data science; Psychology; Medicine; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00046784,0.0003111909,0.0004112858,0.0001948418,0.001189319,0.0000550106,0.0002147047,0.0003770188,0.003557433],"category_scores_gemma":[0.00003210795,0.000272955,0.00007424479,0.0005092636,0.0001477486,0.0001948453,0.00008230333,0.0009828217,0.0003905533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007237758,"about_ca_system_score_gemma":0.00254622,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.02940656,"about_ca_topic_score_gemma":0.02478344,"domain_scores_codex":[0.9968848,0.000198933,0.0007610304,0.0008038376,0.0003363307,0.001015093],"domain_scores_gemma":[0.9980595,0.0003611909,0.0002263615,0.0005164157,0.0002519113,0.0005846421],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001942363,0.00005923328,0.1579003,0.00586423,0.000104189,0.000006484474,0.004533866,0.00005319699,0.001323116,0.003686788,0.3517447,0.4745297],"study_design_scores_gemma":[0.0004861784,0.00003432697,0.09807941,0.001106749,0.00005368126,0.000001346584,0.0004467489,0.0005625492,0.0001601612,0.0004283998,0.898372,0.0002684384],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5994577,0.2491889,0.0005532801,0.1082854,0.01348282,0.01670556,0.003109773,0.001523755,0.007692817],"genre_scores_gemma":[0.6818736,0.0831346,0.0002006518,0.006089664,0.00307502,0.1762542,0.00004939513,0.0001494332,0.04917348],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5466273,"threshold_uncertainty_score":0.9999723,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06268529280993251,"score_gpt":0.4735881040820005,"score_spread":0.410902811272068,"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."}}