{"id":"W2039585895","doi":"10.1080/02687030701282595","title":"Counting what counts: A framework for capturing real‐life outcomes of aphasia intervention","year":2008,"lang":"en","type":"article","venue":"Aphasiology","topic":"Neurobiology of Language and Bilingualism","field":"Neuroscience","cited_by":308,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Western Hospital; University Health Network; Baycrest Hospital; University of Toronto","funders":"","keywords":"Aphasia; Context (archaeology); Intervention (counseling); Psychology; Psychological intervention; Outcome (game theory); Stakeholder; Situated; Conceptual framework; Quality of life (healthcare); Focus group; Applied psychology; Computer science; Cognitive psychology; Psychotherapist; Public relations; Sociology; Artificial intelligence; Political science; Psychiatry","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":[0.0001394426,0.0001006018,0.0002441032,0.00007068954,0.00009222085,0.000007899661,0.000181019,0.0001373761,0.00009896661],"category_scores_gemma":[0.001184953,0.00008301311,0.0001215697,0.00007379682,0.0002393871,0.0001177652,0.00004238403,0.000121558,0.00002914961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008451523,"about_ca_system_score_gemma":0.00002180258,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002112512,"about_ca_topic_score_gemma":0.00000307472,"domain_scores_codex":[0.9991218,0.00007790764,0.0002655037,0.0002638352,0.000062949,0.0002079545],"domain_scores_gemma":[0.9989018,0.0006855293,0.000165383,0.0001802336,0.00003516152,0.00003184931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006599941,0.001080134,0.03442904,0.0004289924,0.0002003619,0.001813547,0.009129678,0.00002813121,0.8561669,0.06266874,0.004241303,0.02915317],"study_design_scores_gemma":[0.01223034,0.004423733,0.03879103,0.001095385,0.0003609955,0.00801362,0.006794564,0.0007044835,0.7604887,0.102378,0.06162108,0.003098093],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966657,0.0002804998,0.001194614,0.0002312625,0.0009891964,0.0001696054,0.00001131769,0.00005675333,0.0004010722],"genre_scores_gemma":[0.9969785,0.0003771819,0.0006589509,0.001675444,0.00009695444,0.00001606166,0.000005753146,0.00001276687,0.0001784443],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09567824,"threshold_uncertainty_score":0.3385176,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06521164190258133,"score_gpt":0.3414669779443981,"score_spread":0.2762553360418167,"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."}}