{"id":"W7111204669","doi":"10.1371/journal.pone.0329794.s002","title":"PRISMA 2020 main checklist.","year":2025,"lang":"","type":"article","venue":"Figshare","topic":"Chronic Disease Management Strategies","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Cluster analysis; Cluster (spacecraft); Latent class model; Population; Multimorbidity; CINAHL; Disease","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.05582235,0.005052095,0.01315633,0.01837228,0.002685724,0.009865349,0.00888089,0.004816945,0.3482698],"category_scores_gemma":[0.1371267,0.005626508,0.01746192,0.02089573,0.003433103,0.00551819,0.00644231,0.007075712,0.03551454],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009663,"about_ca_system_score_gemma":0.04440599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01115704,"about_ca_topic_score_gemma":0.01332628,"domain_scores_codex":[0.9483906,0.01952776,0.02296464,0.003593618,0.004131555,0.001391915],"domain_scores_gemma":[0.8572211,0.0892886,0.01724196,0.01013872,0.02445493,0.00165468],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001190135,0.00009433729,0.0004583389,0.7118061,0.001993252,0.0001877236,0.0009902273,0.0008411346,0.0004454013,0.007790093,0.2520674,0.02213592],"study_design_scores_gemma":[0.0106124,0.0003944397,0.005237153,0.3293601,0.004842566,0.000449638,0.00221221,0.003069145,0.001341541,0.02916072,0.6126896,0.0006306414],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.001374689,0.005356404,0.01370937,0.007040279,0.002769785,0.2929994,0.6575516,0.01048318,0.008715301],"genre_scores_gemma":[0.002266124,0.002396796,0.05803312,0.001013711,0.0001282437,0.9065748,0.0237447,0.001101592,0.004740837],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9441776,"threshold_uncertainty_score":0.9296141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03137966011214884,"score_gpt":0.3200896904919814,"score_spread":0.2887100303798326,"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."}}