{"id":"W4318816590","doi":"10.12688/wellcomeopenres.17148.2","title":"Using self-supervised feature learning to improve the use of pulse oximeter signals to predict paediatric hospitalization","year":2023,"lang":"en","type":"preprint","venue":"Wellcome Open Research","topic":"Non-Invasive Vital Sign Monitoring","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Children's Hospital","funders":"Alliance for Accelerating Excellence in Science in Africa; African Academy of Sciences; New Partnership for Africa's Development; Government of the United Kingdom; Wellcome Trust","keywords":"Artificial intelligence; Logistic regression; Deep learning; Computer science; Feature (linguistics); Machine learning; Feature extraction; Pattern recognition (psychology); Photoplethysmogram; Regression; Statistics; Mathematics; Computer vision","routes":{"ca_aff":true,"ca_fund":false,"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","scholarly_communication","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.003170344,0.0004915555,0.0006596278,0.001151873,0.0003279514,0.00143821,0.0026229,0.0004815981,0.00007271684],"category_scores_gemma":[0.001378114,0.0004284023,0.0001887457,0.002687383,0.00005151762,0.0004224117,0.007087083,0.002379922,0.0002263536],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006240227,"about_ca_system_score_gemma":0.0002104691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001334869,"about_ca_topic_score_gemma":0.00002942642,"domain_scores_codex":[0.9950739,0.0007449219,0.0006258009,0.0009548755,0.001536369,0.001064099],"domain_scores_gemma":[0.996308,0.001150707,0.000104828,0.001307456,0.0007268477,0.0004021499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000885745,0.0000717818,0.02385373,0.001315372,0.0004268459,0.00005312281,0.002634043,0.742809,0.2167867,0.00002610644,0.00759806,0.004336597],"study_design_scores_gemma":[0.00315455,0.002501956,0.03405496,0.00693087,0.0006326052,0.00001527814,0.003170602,0.5619671,0.3492081,0.001549309,0.0309465,0.005868058],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9677809,0.0007299894,0.01345498,0.0007420015,0.002745206,0.01263915,0.0002880969,0.0007092018,0.0009104438],"genre_scores_gemma":[0.9679663,0.0004120681,0.02659919,0.00003807945,0.001302472,0.001083912,0.0001087466,0.000525507,0.001963684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1808419,"threshold_uncertainty_score":0.9999216,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1579754051298883,"score_gpt":0.3545106070535888,"score_spread":0.1965352019237006,"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."}}