{"id":"W4408636042","doi":"10.3389/fped.2025.1569913","title":"Predictive analysis of dominant hand grip strength among young children aged 6–15 years using machine learning techniques: a decision tree and regression analysis","year":2025,"lang":"en","type":"article","venue":"Frontiers in Pediatrics","topic":"Nutrition and Health in Aging","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centennial College","funders":"","keywords":"Grip strength; Anthropometry; Regression analysis; Medicine; Hand strength; Predictive modelling; Decision tree; Linear regression; Physical therapy; Statistics; Demography; Mathematics; Machine learning; Computer science; Internal medicine","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":[],"consensus_categories":[],"category_scores_codex":[0.0007899069,0.0001810784,0.0008953809,0.00519643,0.0001495167,0.00002931751,0.0000967348,0.0002080255,0.00001166855],"category_scores_gemma":[0.0004684185,0.0001644541,0.0002222429,0.005938692,0.0001351991,0.0001019798,0.00008682899,0.000490129,7.671858e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002412887,"about_ca_system_score_gemma":0.0001033857,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006795543,"about_ca_topic_score_gemma":0.0003561025,"domain_scores_codex":[0.9981725,0.0001478033,0.0006468299,0.0004105881,0.0003648143,0.0002574933],"domain_scores_gemma":[0.9990171,0.0001387582,0.0003314569,0.0002580335,0.0001086153,0.0001460499],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003939716,0.0001072267,0.9915218,0.0001012164,0.0006881818,0.00002099457,0.0005387914,0.0003280316,0.00009332156,0.000002633071,0.0005593815,0.005644465],"study_design_scores_gemma":[0.001606133,0.00008491159,0.9077672,0.0002237094,0.008372281,0.000002001327,0.0003998751,0.08106333,0.0002452419,0.00008107264,0.00003343396,0.0001208222],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8910408,0.004338704,0.1038893,0.00003134205,0.0001562823,0.0003860977,0.00004359485,0.00003070019,0.00008312229],"genre_scores_gemma":[0.9375538,0.003962382,0.05807485,0.00003993739,0.00006520119,0.000009583108,0.0001608127,0.00001439381,0.0001190197],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0837546,"threshold_uncertainty_score":0.6706243,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007804396067818413,"score_gpt":0.290318299577422,"score_spread":0.2825139035096036,"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."}}