{"id":"W4294391398","doi":"10.22159/ijpps.2022v14i9.44728","title":"PREPARATION, CHARACTERIZATION, AND OPTIMIZATION OF MEBENDAZOLE SPHERICAL AGGLOMERATES USING MODIFIED EVAPORATIVE PRECIPITATION IN AQUEOUS SOLUTION (EPAS)","year":2022,"lang":"en","type":"article","venue":"International Journal of Pharmacy and Pharmaceutical Sciences","topic":"Drug Solubulity and Delivery Systems","field":"Pharmacology, Toxicology and Pharmaceutics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University Health Network","funders":"All India Council for Technical Education","keywords":"Mebendazole; Agglomerate; Materials science; Dissolution; Aqueous solution; Solubility; Precipitation; Chromatography; Differential scanning calorimetry; Chemical engineering; Nuclear chemistry; Chemistry; Composite material; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004174459,0.0006520437,0.0003509055,0.000399055,0.0002288627,0.0002213788,0.0002699554,0.0003323311,0.0006207624],"category_scores_gemma":[0.0003254172,0.0002039265,0.0005813113,0.0002961202,0.0002299766,0.0001827172,0.0001865264,0.000248258,0.0005513839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004035877,"about_ca_system_score_gemma":0.0002121765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001149344,"about_ca_topic_score_gemma":0.003088196,"domain_scores_codex":[0.999701,0.00004454825,0.0000307556,0.00005795627,0.0001380412,0.00002779947],"domain_scores_gemma":[0.9998614,0.00002231417,0.00004092635,0.00001223453,0.00005355363,0.000009633868],"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":[0.00003455758,0.00002177863,0.00009013004,0.00007665062,0.000007155823,0.00003073057,0.00001537498,0.0004886747,0.9966427,0.00003220755,0.00004257429,0.002517506],"study_design_scores_gemma":[0.00001449349,0.0003497619,0.001754943,0.000006026157,0.00001362712,0.00005793335,0.00001153497,0.001543537,0.9939745,0.00001341164,0.002252385,0.000007907407],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9476908,0.004515592,0.04263191,0.0002008559,0.00006317209,0.0004580087,0.000836806,0.0003301434,0.003272614],"genre_scores_gemma":[0.9261627,0.00292698,0.0624284,0.00009091041,0.00002066899,0.0004512649,0.001170295,0.000129774,0.006619035],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001149344,"threshold_uncertainty_score":0.002928197,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1531258025540232,"score_gpt":0.4763409432970208,"score_spread":0.3232151407429976,"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."}}