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{"title"=>"Collective intelligence meets medical decision-making: The collective outperforms the best radiologist", "type"=>"journal", "authors"=>[{"first_name"=>"Max", "last_name"=>"Wolf", "scopus_author_id"=>"36802674500"}, {"first_name"=>"Jens", "last_name"=>"Krause", "scopus_author_id"=>"7202955124"}, {"first_name"=>"Patricia A.", "last_name"=>"Carney", "scopus_author_id"=>"35598907200"}, {"first_name"=>"Andy", "last_name"=>"Bogart", "scopus_author_id"=>"24381462200"}, {"first_name"=>"Ralf H.J.M.", "last_name"=>"Kurvers", "scopus_author_id"=>"24171332900"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84942849529", "pmid"=>"26267331", "pui"=>"606195927", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "scopus"=>"2-s2.0-84942849529", "doi"=>"10.1371/journal.pone.0134269", "issn"=>"19326203"}, "id"=>"8327ecfe-6b96-3487-a8af-6e47c6d15837", "abstract"=>"While collective intelligence (CI) is a powerful approach to increase decision accuracy, few attempts have been made to unlock its potential in medical decision-making. Here we investigated the performance of three well-known collective intelligence rules (\"majority\", \"quorum\", and \"weighted quorum\") when applied to mammography screening. For any particular mammogram, these rules aggregate the independent assessments of multiple radiologists into a single decision (recall the patient for additional workup or not). We found that, compared to single radiologists, any of these CI-rules both increases true positives (i.e., recalls of patients with cancer) and decreases false positives (i.e., recalls of patients without cancer), thereby overcoming one of the fundamental limitations to decision accuracy that individual radiologists face. Importantly, we find that all CI-rules systematically outperform even the best-performing individual radiologist in the respective group. Our findings demonstrate that CI can be employed to improve mammography screening; similarly, CI may have the potential to improve medical decision-making in a much wider range of contexts, including many areas of diagnostic imaging and, more generally, diagnostic decisions that are based on the subjective interpretation of evidence.", "link"=>"http://www.mendeley.com/research/collective-intelligence-meets-medical-decisionmaking-collective-outperforms-best-radiologist", "reader_count"=>39, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>3, "Researcher"=>7, "Student > Ph. D. Student"=>10, "Student > Master"=>4, "Other"=>1, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_user_role"=>{"Unspecified"=>4, "Professor > Associate Professor"=>2, "Student > Doctoral Student"=>3, "Researcher"=>7, "Student > Ph. D. Student"=>10, "Student > Master"=>4, "Other"=>1, "Student > Bachelor"=>3, "Lecturer > Senior Lecturer"=>1, "Professor"=>4}, "reader_count_by_subject_area"=>{"Engineering"=>1, "Unspecified"=>5, "Biochemistry, Genetics and Molecular Biology"=>1, "Agricultural and Biological Sciences"=>7, "Medicine and Dentistry"=>6, "Arts and Humanities"=>1, "Psychology"=>7, "Social Sciences"=>1, "Computer Science"=>8, "Economics, Econometrics and Finance"=>2}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>6}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>7}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>2}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>7}, "Computer Science"=>{"Computer Science"=>8}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Arts and Humanities"=>{"Arts and Humanities"=>1}}, "group_count"=>2}

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• http://doi.org/10.1001/jamanetworkopen.2019.0096
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• {"id"=>"632102331790827520", "text"=>"great interdisciplinary research @LeibnizIGB #PLOSONE: Collective Intelligence Meets Medical Decision-Making http://t.co/xUbyFHZ59V", "created_at"=>"2015-08-14T08:11:54Z", "user"=>"gkalinkat", "user_name"=>"Gregor Kalinkat", "user_profile_image"=>"http://pbs.twimg.com/profile_images/540133656795181056/HbficDeJ_normal.jpeg"}
• {"id"=>"632141016833896448", "text"=>"Collective Intelligence Meets Medical Decision-Making http://t.co/SvpbAw6EW9\nTelemedicine improves medical decision http://t.co/VMcjiS6lgO", "created_at"=>"2015-08-14T10:45:37Z", "user"=>"owmn_org", "user_name"=>"OWMN", "user_profile_image"=>"http://pbs.twimg.com/profile_images/3716500714/a7f9112535552c62a8f6408996fa4caf_normal.jpeg"}
• {"id"=>"632285936311668736", "text"=>"Collective Intelligence Meets Medical Decision-Making: The Collective Outperforms the Best Radiologist\nhttp://t.co/8bUyJlFQme #breastcancer", "created_at"=>"2015-08-14T20:21:29Z", "user"=>"PinkRibbon411", "user_name"=>"ES", "user_profile_image"=>"http://pbs.twimg.com/profile_images/503703089077956608/UN4cO3A5_normal.jpeg"}
• {"id"=>"633738463884414977", "text"=>"RT @gkalinkat: great interdisciplinary research @LeibnizIGB #PLOSONE: Collective Intelligence Meets Medical Decision-Making http://t.co/xUb…", "created_at"=>"2015-08-18T20:33:18Z", "user"=>"CognitionAction", "user_name"=>"Cognition_In_Action", "user_profile_image"=>"http://pbs.twimg.com/profile_images/474497434249228288/9amZ9kCL_normal.jpeg"}
• {"id"=>"633946233757958144", "text"=>"Collective Intelligence Meets Medical Decision-Making: The Collective Outperforms the Best Radiologist: http://t.co/OS70x1Htc7", "created_at"=>"2015-08-19T10:18:54Z", "user"=>"fusaroli", "user_name"=>"Riccardo Fusaroli", "user_profile_image"=>"http://pbs.twimg.com/profile_images/378800000829262066/67cb71d9f4900aa0d5035380291bd615_normal.jpeg"}
• {"id"=>"633966951870558208", "text"=>"RT @fusaroli: Collective Intelligence Meets Medical Decision-Making: The Collective Outperforms the Best Radiologist: http://t.co/OS70x1Htc7", "created_at"=>"2015-08-19T11:41:14Z", "user"=>"Nicolas_Fay", "user_name"=>"nicolas fay", "user_profile_image"=>"http://pbs.twimg.com/profile_images/421105903819968512/gLPk36Pe_normal.jpeg"}
• {"id"=>"633972047052447744", "text"=>"RT @fusaroli: Collective Intelligence Meets Medical Decision-Making: The Collective Outperforms the Best Radiologist: http://t.co/OS70x1Htc7", "created_at"=>"2015-08-19T12:01:29Z", "user"=>"leoschilbach", "user_name"=>"Leonhard Schilbach", "user_profile_image"=>"http://pbs.twimg.com/profile_images/631074841509109760/ZWRM5T9X_normal.jpg"}
• {"id"=>"635946345006653440", "text"=>"#PLOSONE: Collective Intelligence Meets Medical Decision-Making made me think of Human Dx @DrNundy http://t.co/zb1hDVJXKD", "created_at"=>"2015-08-24T22:46:38Z", "user"=>"UrmimalaSarkar", "user_name"=>"Urmimala Sarkar, MD", "user_profile_image"=>"http://pbs.twimg.com/profile_images/515269256124981250/0xn7Lm3z_normal.jpeg"}
• {"id"=>"636121887500861440", "text"=>"http://t.co/Vgy39xKqaH interesting paper shows that radiologists make better judgements as groups. #bioscience #collectivedecisions #quorum", "created_at"=>"2015-08-25T10:24:11Z", "user"=>"Domburnsburns", "user_name"=>"Dom Burns", "user_profile_image"=>"http://pbs.twimg.com/profile_images/557304336301039616/RWv-iovV_normal.jpeg"}
• {"id"=>"641667619729317888", "text"=>"http://t.co/8toQKDx5rm", "created_at"=>"2015-09-09T17:40:56Z", "user"=>"JAVEGI", "user_name"=>"EnfySegdelPaciente", "user_profile_image"=>"http://pbs.twimg.com/profile_images/539408136114028544/L2-qmp--_normal.jpeg"}
• {"id"=>"642009121592606720", "text"=>"@slatestarcodex http://t.co/oAiJ32c4hf", "created_at"=>"2015-09-10T16:17:57Z", "user"=>"bswud", "user_name"=>"Ben Southwood", "user_profile_image"=>"http://pbs.twimg.com/profile_images/622521661192077312/Yx5ope4R_normal.jpg"}
• {"id"=>"642029023116308480", "text"=>"RT @bswud: @slatestarcodex http://t.co/oAiJ32c4hf", "created_at"=>"2015-09-10T17:37:02Z", "user"=>"bechhof", "user_name"=>"Nathaniel Bechhofer", "user_profile_image"=>"http://pbs.twimg.com/profile_images/617750652899487744/_QQck9gO_normal.jpg"}
• {"id"=>"661054078835941376", "text"=>"El equipo supera al mejor radiólogo #SegPac #Ptsafety \nhttps://t.co/n9KmBAHgGo", "created_at"=>"2015-11-02T05:35:48Z", "user"=>"AEGRIS2", "user_name"=>"AEGRIS", "user_profile_image"=>"http://pbs.twimg.com/profile_images/3441833900/a028ec57f9e578b90ede6cbb905bf915_normal.jpeg"}
• {"id"=>"727062135621193728", "text"=>"Collective Intelligence Meets Medical Decision-Making: The Collective Outperforms the Best Radiologist https://t.co/IFy5lAsEOw @PLOSONE", "created_at"=>"2016-05-02T09:08:15Z", "user"=>"arturogff", "user_name"=>"Arturo G. Ferrer", "user_profile_image"=>"http://pbs.twimg.com/profile_images/543781571929456641/dUUYM7wH_normal.png"}
• {"id"=>"727064735200501760", "text"=>"RT @arturogff: Collective Intelligence Meets Medical Decision-Making: The Collective Outperforms the Best Radiologist https://t.co/IFy5lAsE…", "created_at"=>"2016-05-02T09:18:35Z", "user"=>"juliomayol", "user_name"=>"Julio Mayol", "user_profile_image"=>"http://pbs.twimg.com/profile_images/702422162616008704/7h0W43bb_normal.jpg"}
• {"id"=>"727065689329143808", "text"=>"RT @arturogff: Collective Intelligence Meets Medical Decision-Making: The Collective Outperforms the Best Radiologist https://t.co/IFy5lAsE…", "created_at"=>"2016-05-02T09:22:23Z", "user"=>"InsenserTech", "user_name"=>"Jose Insenser (Tech)", "user_profile_image"=>"http://pbs.twimg.com/profile_images/606898249455312896/iSiqudfb_normal.jpg"}
• {"id"=>"757056347435327488", "text"=>"Collective Intelligence Meets Medical Decision-Making:Collective Outperforms Best Radiologist.Useful 4 subjectives\n\n https://t.co/xvs01FGdAN", "created_at"=>"2016-07-24T03:34:33Z", "user"=>"JockMcClellan", "user_name"=>"Jock McClellan", "user_profile_image"=>"http://pbs.twimg.com/profile_images/474370611918016512/-gvkrhEu_normal.jpeg"}
• {"id"=>"757684164866998273", "text"=>"RT @JockMcClellan: Collective Intelligence Meets Medical Decision-Making:Collective Outperforms Best Radiologist.Useful 4 subjectives\n\n htt…", "created_at"=>"2016-07-25T21:09:16Z", "user"=>"CICWCat", "user_name"=>"CICWCat", "user_profile_image"=>"http://pbs.twimg.com/profile_images/704669689201295360/0F8e1jGM_normal.jpg"}
• {"id"=>"784205431967145984", "text"=>"Would be cool to study the real-world use of collective intelligence in radiology https://t.co/B24q2u1DqQ… https://t.co/yFUiT6Nnax", "created_at"=>"2016-10-07T01:35:19Z", "user"=>"HardeepSinghMD", "user_name"=>"Hardeep Singh MD", "user_profile_image"=>"http://pbs.twimg.com/profile_images/525269944137564161/xtZ1dwYr_normal.jpeg"}
• {"id"=>"934278955607007232", "text"=>"Lots of radiologists outperform the best radiologist. \nhttps://t.co/GTuYLGH0zp", "created_at"=>"2017-11-25T04:33:55Z", "user"=>"timvangelder", "user_name"=>"Tim van Gelder", "user_profile_image"=>"http://pbs.twimg.com/profile_images/829895282015903744/DmM7U4kK_normal.jpg"}
• {"id"=>"934288433652162560", "text"=>"RT @timvangelder: Lots of radiologists outperform the best radiologist. \nhttps://t.co/GTuYLGH0zp", "created_at"=>"2017-11-25T05:11:35Z", "user"=>"BrianSJ3", "user_name"=>"BrianSJ", "user_profile_image"=>"http://pbs.twimg.com/profile_images/1242991852/BSJ_CBC_100x100_normal.jpg"}
• {"id"=>"934729405263265792", "text"=>"RT @timvangelder: Lots of radiologists outperform the best radiologist. \nhttps://t.co/GTuYLGH0zp", "created_at"=>"2017-11-26T10:23:51Z", "user"=>"jeremyacb", "user_name"=>"Jeremy Bean", "user_profile_image"=>"http://pbs.twimg.com/profile_images/617593451379601408/1O2r1AMW_normal.jpg"}
• {"id"=>"935655859153264646", "text"=>"RT @timvangelder: Lots of radiologists outperform the best radiologist. \nhttps://t.co/GTuYLGH0zp", "created_at"=>"2017-11-28T23:45:15Z", "user"=>"ctsinclair", "user_name"=>"Christian Sinclair, MD, FAAHPM", "user_profile_image"=>"http://pbs.twimg.com/profile_images/799086391221067777/9trLk4H3_normal.jpg"}
• {"id"=>"935656358438768641", "text"=>"RT @timvangelder: Lots of radiologists outperform the best radiologist. \nhttps://t.co/GTuYLGH0zp", "created_at"=>"2017-11-28T23:47:14Z", "user"=>"keeweedoc", "user_name"=>"Andre", "user_profile_image"=>"http://pbs.twimg.com/profile_images/3377102649/04a054623febdb5ee59b1cf8f6dea5a7_normal.png"}

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Figshare

• {"files"=>["https://ndownloader.figshare.com/files/2210616"], "description"=>"<div><p>While collective intelligence (CI) is a powerful approach to increase decision accuracy, few attempts have been made to unlock its potential in medical decision-making. Here we investigated the performance of three well-known collective intelligence rules (“majority”, “quorum”, and “weighted quorum”) when applied to mammography screening. For any particular mammogram, these rules aggregate the independent assessments of multiple radiologists into a single decision (recall the patient for additional workup or not). We found that, compared to single radiologists, any of these CI-rules both increases true positives (i.e., recalls of patients with cancer) and decreases false positives (i.e., recalls of patients without cancer), thereby overcoming one of the fundamental limitations to decision accuracy that individual radiologists face. Importantly, we find that all CI-rules systematically outperform even the best-performing individual radiologist in the respective group. Our findings demonstrate that CI can be employed to improve mammography screening; similarly, CI may have the potential to improve medical decision-making in a much wider range of contexts, including many areas of diagnostic imaging and, more generally, diagnostic decisions that are based on the subjective interpretation of evidence.</p></div>", "links"=>[], "tags"=>["increase decision accuracy", "intelligence", "mammography screening", "radiologist", "positive", "collective", "ci", "i.e", "quorum"], "article_id"=>1509625, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Max Wolf", "Jens Krause", "Patricia A. Carney", "Andy Bogart", "Ralf H. J. M. Kurvers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0134269", "stats"=>{"downloads"=>2, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Collective_Intelligence_Meets_Medical_Decision_Making_The_Collective_Outperforms_the_Best_Radiologist_/1509625", "title"=>"Collective Intelligence Meets Medical Decision-Making: The Collective Outperforms the Best Radiologist", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-08-12 04:00:15"}
• {"files"=>["https://ndownloader.figshare.com/files/2210609"], "description"=>"<p>All three CI-rules overcome the trade-off between true and false positives that single radiologists face, and outperform the best radiologist within each group. Shown are mean (± SEM) true positives (A), false positives (B) and accuracy (C) of the three CI-rules, as a function of group size <i>n</i>. The dashed line corresponds to the average individual performance of radiologists (i.e., group size of 1), the green dots correspond to the highest-performing radiologist for a given group size <i>n</i>.</p>", "links"=>[], "tags"=>["increase decision accuracy", "intelligence", "mammography screening", "radiologist", "positive", "collective", "ci", "i.e", "quorum"], "article_id"=>1509618, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Max Wolf", "Jens Krause", "Patricia A. Carney", "Andy Bogart", "Ralf H. J. M. Kurvers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0134269.g001", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_All_three_CI_rules_outperform_the_best_radiologist_/1509618", "title"=>"All three CI-rules outperform the best radiologist.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-12 04:00:15"}
• {"files"=>["https://ndownloader.figshare.com/files/2210611"], "description"=>"<p>Each dot is obtained by setting a different fixed quorum threshold, starting at 0 with increments of 0.05 up to 1. Data are based on a group size of 15 and 2,500 simulations (see main text). The majority rule corresponds to a fixed quorum threshold of 0.5. Note that, while we here consider the consequences of fixed quorum thresholds, the analyses in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134269#pone.0134269.g001\" target=\"_blank\">Fig 1</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134269#pone.0134269.g003\" target=\"_blank\">Fig 3</a> are based on flexible quorum thresholds that are estimated from a training set (see main text).</p>", "links"=>[], "tags"=>["increase decision accuracy", "intelligence", "mammography screening", "radiologist", "positive", "collective", "ci", "i.e", "quorum"], "article_id"=>1509620, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Max Wolf", "Jens Krause", "Patricia A. Carney", "Andy Bogart", "Ralf H. J. M. Kurvers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0134269.g002", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_ROC_curve_for_the_quorum_rule_and_the_weighted_quorum_rule_/1509620", "title"=>"ROC curve for the quorum rule and the weighted quorum rule.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-12 04:00:15"}
• {"files"=>["https://ndownloader.figshare.com/files/2210613"], "description"=>"<p>The three panels correspond to the three illustrative scenarios where the goal was to: (A) maximize gains in true positives while keeping the false positive rate (FPR) close to the average false positive rate of 0.336 in the data set; (B) minimize the false positive rate while keeping the true positive rate (TPR) at the average true positive rate of 0.762 in the data set; and (C) maximize overall accuracy. As can be seen, on top of the gains achieved by a quorum threshold set halfway between the average true and false positive rate (black dots, corresponding to the values from <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134269#pone.0134269.g001\" target=\"_blank\">Fig 1</a>), further improvements in true positives (A), false positives (B) or overall accuracy (C) can be achieved by fine-tuning the quorum threshold appropriately. Shown are mean (± SEM).</p>", "links"=>[], "tags"=>["increase decision accuracy", "intelligence", "mammography screening", "radiologist", "positive", "collective", "ci", "i.e", "quorum"], "article_id"=>1509622, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Max Wolf", "Jens Krause", "Patricia A. Carney", "Andy Bogart", "Ralf H. J. M. Kurvers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0134269.g003", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_quorum_rule_can_be_fine_tuned_to_put_more_weight_on_improving_true_positives_false_positives_or_overall_accuracy_/1509622", "title"=>"The quorum rule can be fine-tuned to put more weight on improving true positives, false positives or overall accuracy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-12 04:00:15"}
• {"files"=>["https://ndownloader.figshare.com/files/2210614"], "description"=>"<p>¹ Note that these CI-rules are a sequence of increasingly complex rules: the majority rule is a special case of the quorum rule with the quorum threshold set to 0.5, and the quorum rule is a special case of the weighted quorum rule with the individual weights set to 1.0.</p><p>Three CI-rules<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134269#t001fn001\" target=\"_blank\">¹</a>.</p>", "links"=>[], "tags"=>["increase decision accuracy", "intelligence", "mammography screening", "radiologist", "positive", "collective", "ci", "i.e", "quorum"], "article_id"=>1509623, "categories"=>["Biological Sciences", "Science Policy"], "users"=>["Max Wolf", "Jens Krause", "Patricia A. Carney", "Andy Bogart", "Ralf H. J. M. Kurvers"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0134269.t001", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Three_CI_rules_1_/1509623", "title"=>"Three CI-rules¹.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-08-12 04:00:15"}

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• {"unique-ip"=>"6", "full-text"=>"7", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"3"}
• {"unique-ip"=>"10", "full-text"=>"11", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"4"}
• {"unique-ip"=>"23", "full-text"=>"22", "pdf"=>"6", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2019", "month"=>"5"}
• {"unique-ip"=>"12", "full-text"=>"20", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"2", "supp-data"=>"0", "cited-by"=>"1", "year"=>"2019", "month"=>"8"}
• {"unique-ip"=>"9", "full-text"=>"9", "pdf"=>"4", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"9"}
• {"unique-ip"=>"5", "full-text"=>"4", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"10"}
• {"unique-ip"=>"12", "full-text"=>"6", "pdf"=>"9", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2019", "month"=>"12"}
• {"unique-ip"=>"10", "full-text"=>"10", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"2"}
• {"unique-ip"=>"16", "full-text"=>"16", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"1", "year"=>"2020", "month"=>"3"}
• {"unique-ip"=>"13", "full-text"=>"10", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"4"}
• {"unique-ip"=>"23", "full-text"=>"23", "pdf"=>"8", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"1", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"5"}
• {"unique-ip"=>"13", "full-text"=>"15", "pdf"=>"3", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"6"}
• {"unique-ip"=>"1", "full-text"=>"1", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"7"}
• {"unique-ip"=>"2", "full-text"=>"1", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"8"}
• {"unique-ip"=>"1", "full-text"=>"0", "pdf"=>"1", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"9"}
• {"unique-ip"=>"17", "full-text"=>"18", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"0", "cited-by"=>"0", "year"=>"2020", "month"=>"10"}
• {"unique-ip"=>"7", "full-text"=>"11", "pdf"=>"2", "scanned-summary"=>"0", "scanned-page-browse"=>"0", "figure"=>"0", "supp-data"=>"1", "cited-by"=>"0", "year"=>"2020", "month"=>"11"}

Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}

F1000Prime