{"id":"W4409893893","doi":"10.3390/s25092762","title":"Impact of Temporal Resolution on Autocorrelative Features of Cerebral Physiology from Invasive and Non-Invasive Sensors in Acute Traumatic Neural Injury: Insights from the CAHR-TBI Cohort","year":2025,"lang":"en","type":"article","venue":"Sensors","topic":"Traumatic Brain Injury and Neurovascular Disturbances","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary; Université de Montréal; Hotchkiss Brain Institute; University of British Columbia; Pan Am Clinic; University of Manitoba","funders":"Canadian Institutes of Health Research; Svenska Läkaresällskapet; Svenska Kulturfonden; Research Manitoba; Health Sciences Centre Foundation; University of Manitoba; Hjärnfonden; Finska Läkaresällskapet; Natural Sciences and Engineering Research Council of Canada; Karolinska Institutet; Familjen Erling-Perssons Stiftelse","keywords":"Autoregressive integrated moving average; Traumatic brain injury; Computer science; Autocorrelation; Population; Temporal resolution; Artificial intelligence; Autoregressive model; Data mining; Machine learning; Time series; Medicine; Statistics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00291802,0.0002716312,0.0002839868,0.0005867439,0.0003790292,0.0008979967,0.0005290696,0.000285017,0.001222652],"category_scores_gemma":[0.0128682,0.0001218054,0.0005657131,0.000998517,0.0004211248,0.0003899362,0.0007407057,0.0006629849,0.000194719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009711494,"about_ca_system_score_gemma":0.002337518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1507108,"about_ca_topic_score_gemma":0.2119811,"domain_scores_codex":[0.999134,0.0002200346,0.00007308536,0.0001878244,0.000253075,0.0001319513],"domain_scores_gemma":[0.9965323,0.001357706,0.0006275404,0.0005917485,0.0007063547,0.0001842472],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00086498,0.00005333611,0.9345849,0.00009423496,0.0004361877,0.0004130721,0.0009965986,0.004611075,0.00249427,0.001193202,0.001631718,0.05262638],"study_design_scores_gemma":[0.00000593817,0.00006269222,0.9897132,0.00003536298,0.0001294706,0.0003305008,0.0006684909,0.0059572,0.0006325078,0.0005025473,0.00193723,0.00002473435],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9884146,0.0008438279,0.005715503,0.0004438388,0.00002735498,0.00002281879,0.002640786,0.00003793103,0.001853352],"genre_scores_gemma":[0.9953436,0.0003790513,0.001937566,0.00004989931,0.00001657963,0.00001723128,0.001876171,0.00002462285,0.0003550843],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1507108,"threshold_uncertainty_score":0.2996672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01296053728131871,"score_gpt":0.2781333747107556,"score_spread":0.2651728374294369,"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."}}