Tracing the path of 37,050 studies into practice across 18 specialties of the 2.4 million published between 2011 and 2020
- Department of Surgery, Harvard Medical School, Massachusetts General Hospital, Canada
- Computational Statistics and Machine Learning Group, Department of Statistics, University of Oxford, United Kingdom
- Department of Computer Science, University of Toronto, Canada
- Institute for Better Health, Trillium Health Partners, Canada
Figures
Figure 1 with 1 supplement
Citations in UpToDate over the past decade as a function of article type, grouped by specialty.
(A) Stacked barplot of the absolute number of citations in UpToDate over the past decade as a function of article type, grouped by specialty (inset: zoomed plot to permit comparison of non-medicine specialties). (B) Barplot of the % proportion of articles cited (defined as number of particular article type cited/total number of articles cited in UpToDate) ×100, grouped by medical specialty. (NB:) Totals in panel A may differ slightly from the totals described in the text as some article may have more than one classification and some references are unclassified.
Figure 1—figure supplement 1
Boxplot of time-to-citation in UpToDate from first publication date as a function of article type, grouped by medical specialty.
Figure 2 with 1 supplement
Proportion of articles cited as a function of the journal’s 5 year impact factor.
Scatterplot of proportion of articles cited, defined as the number of citations/total number of articles published by a specific journal, as a function of the journal’s 5 year impact factor for (A) anesthesiology, (B) cardiac and cardiovascular systems, (C) clinical neurology, (D) critical care medicine, (E) dermatology, (F) emergency medicine, (G) endocrinology and metabolism, (H) gastroenterology and hepatology, (I) geriatrics and gerontology, (J) hematology, (K) infectious diseases, (L) medicine (general and internal), (M) oncology, (N) pathology, (O) pediatrics, (P) ‘radiology, nuclear medicine and medical imaging,’ (Q) rheumatology, and (R) urology and nephrology.
Figure 2—figure supplement 1
Median time-to-citation as a function of the journal’s 5 year impact factor.
Scatterplot of median time-to-citation as a function of the journal’s 5 year impact factor, for (A) anesthesiology, (B) cardiac and cardiovascular systems, (C) clinical neurology, (D) critical care medicine, (E) dermatology, (F) emergency medicine, (G) endocrinology and metabolism, (H) gastroenterology and hepatology, (I) geriatrics and gerontology, (J) hematology, (K) infectious diseases, (L) medicine (general and internal), (M) oncology, (N) pathology, (O) pediatrics, (P) ‘radiology, nuclear medicine and medical imaging’, (Q) rheumatology, and (R) urology and nephrology. An empty plot panel denotes no term, or Unified Medical Language System (UMLS) concept was significant after multiple testing adjustment, and a blank placeholder was placed to explicitly confirm this.
Figure 3 with 4 supplements
Word cloud plots of overrepresented words.
Word cloud plots with size of word proportional to the strength of the association for (A) Unified Medical Language System (UMLS) concepts significantly overrepresented in articles cited from cardiac and cardiovascular systems (i.e. ‘cardiology’ journals); (B) UMLS concepts significantly overrepresented in articles cited from endocrinology and metabolism journals.
Figure 3—figure supplement 1
Word cloud plots of overrepresented terms.
Word cloud plots with size of term proportional to the strength of the association for Unified Medical Language System (UMLS) concepts significantly overrepresented in articles cited from: (A) anesthesiology, (B) cardiac and cardiovascular systems, (C) clinical neurology, (D) critical care medicine, (E) dermatology, (F) emergency medicine, (G) endocrinology and metabolism, (H) gastroenterology and hepatology, (I) geriatrics and gerontology, (J) hematology, (K) infectious diseases, (L) medicine (general and internal), (M) oncology, (N) pathology, (O) pediatrics, (P) ‘radiology, nuclear medicine and medical imaging,’ (Q) rheumatology, and (R) urology and nephrology. An empty plot panel denotes no term, or UMLS concept was significant after multiple testing adjustment, and a blank placeholder was placed to explicitly confirm this.
Figure 3—figure supplement 2
Word cloud plots of significantly underrepresented terms.
Word cloud plots with size of word proportional to the strength of the association for Unified Medical Language System (UMLS) concepts significantly underrepresented in articles cited from: (A) anesthesiology, (B) cardiac and cardiovascular systems, (C) clinical neurology, (D) critical care medicine, (E) dermatology, (F) emergency medicine, (G) endocrinology and metabolism, (H) gastroenterology and hepatology, (I) geriatrics and gerontology, (J) hematology, (K) infectious diseases, (L) medicine (general and internal), (M) oncology, (N) pathology, (O) pediatrics, (P) ‘radiology, nuclear medicine and medical imaging,’ (Q) rheumatology, and (R) urology and nephrology. An empty plot panel denotes no term, or UMLS concept was significant after multiple testing adjustment, and a blank placeholder was placed to explicitly confirm this.
Figure 3—figure supplement 3
Word cloud plots of terms associated a shorter time-to-citation.
Word cloud plots with size of term proportional to the strength of the association for Unified Medical Language System (UMLS) concepts significantly associated a shorter time-to-citation among articles cited from: (A) anesthesiology, (B) cardiac and cardiovascular systems, (C) clinical neurology, (D) critical care medicine, (E) dermatology, (F) emergency medicine, (G) endocrinology and metabolism, (H) gastroenterology and hepatology, (I) geriatrics and gerontology, (J) hematology, (K) infectious diseases, (L) medicine (general and internal), (M) oncology, (N) pathology, (O) pediatrics, (P) ‘radiology, nuclear medicine and medical imaging,’ (Q) rheumatology, and (R) urology and nephrology. An empty plot panel denotes no term, or UMLS concept was significant after multiple testing adjustment, and a blank placeholder was placed to explicitly confirm this.
Figure 3—figure supplement 4
Word cloud plots of terms significantly associated a longer time-to-citation.
Word cloud plots with size of term proportional to the strength of the association for Unified Medical Language System (UMLS) concepts significantly associated a longer time-to-citation among articles cited from: (A) anesthesiology, (B) cardiac and cardiovascular systems, (C) clinical neurology, (D) critical care medicine, (E) dermatology, (F) emergency medicine, (G) endocrinology and metabolism, (H) gastroenterology and hepatology, (I) geriatrics and gerontology, (J) hematology, (K) infectious diseases, (L) medicine (general and internal), (M) oncology, (N) pathology, (O) pediatrics, (P) ‘radiology, nuclear medicine and medical imaging,’ (Q) rheumatology, and (R) urology and nephrology. An empty plot panel denotes no term, or UMLS concept was significant after multiple testing adjustment, and a blank placeholder was placed to explicitly confirm this.
Figure 4 with 1 supplement
Scatterplot of absolute number of articles cited, per city, as a function of the average 10 year cumulative NIH funding (i.e. all citations in all journals across all specialties).
It is important to note that this includes all NIH departments (including all medical and surgical specialties, as well as more translational/basic funding departments).
Figure 4—figure supplement 1
Median time-to-citation, per city, as a function of the average 10 year department-specific NIH funding.
Scatterplots of median time-to-citation, per city, as a function of the average 10 year department-specific NIH funding, for (A) anesthesiology, (B) clinical neurology, (C) dermatology, (D) emergency medicine, (E) infectious diseases, (F) medicine (general and internal), (G) pathology, (H) pediatrics, (I) ‘radiology, nuclear medicine and medical imaging,’ and (J) urology and nephrology. (NB:) The ‘medicine, general and internal’ plot, in panel F, combines the results for nine specialties (cardiac and cardiovascular systems, critical care medicine, endocrinology and metabolism, gastroenterology and hepatology, geriatrics and gerontology, hematology, rheumatology, and oncology – in addition to ‘general and internal medicine’) because a large portion of the funding for these eight specialties occurs through the NIH department combining name of ‘internal medicine/medicine’ (i.e. there was no specific department labels for these subset of specialties).
Figure 5 with 2 supplements
Number of articles cited, per city, as a function of the average 10 year department-specific NIH funding.
Scatterplots of absolute number of articles cited, per city, as a function of the average 10 year department-specific NIH funding, for (A) anesthesiology, (B) clinical neurology, (C) dermatology, (D) emergency medicine, (E) infectious diseases, (F) medicine (general and internal), (G) pathology, (H) pediatrics, (I) ‘radiology, nuclear medicine and medical imaging,’ and (J) urology and nephrology. (NB:) The ‘medicine, general and internal’ plot, in panel F, combines the results for nine specialties (cardiac and cardiovascular systems, critical care medicine, endocrinology and metabolism, gastroenterology and hepatology, geriatrics and gerontology, hematology, rheumatology, and oncology – in addition to ‘general and internal medicine’) because a large portion of the funding for these eight specialties occurs through the NIH department combining name of ‘internal medicine/medicine’ (i.e. there was no specific department labels for these subset of specialties). It is also important to note that relevant funding may come from other NIH departments (e.g. ‘cell biology’), and this is reflected in Figure 4.
Figure 5—figure supplement 1
Proportion of articles cited by city and country.
Barplot summarizing the proportion of articles cited from (A) all countries with at least 10 articles cited over the past decade from the 18 medical specialties; and (B) all American cities with at least 10 articles cited over the past decade from the 18 medical specialties, organized in alphabetical order. Approximately 18% of articles had no primary affiliation.
Figure 5—figure supplement 2
Density maps illustrating the primary affiliation of articles in cited in UpToDate.
(A) Density world map illustrating the primary affiliation of articles in cited in UpToDate for all 18 medical subspecialties. (B) Density map of the United States illustrating the primary affiliation of articles in cited in UpToDate for all 18 medical subspecialties.
Additional files
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Supplementary file 1
Tabulated summary of the clinical relevancy index (CRI) and clinical immediacy index (CII) for all journals, in all 18 medical specialties, rank-ordered by 5 year impact factor.
- https://cdn.elifesciences.org/articles/82498/elife-82498-supp1-v3.docx
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MDAR checklist
- https://cdn.elifesciences.org/articles/82498/elife-82498-mdarchecklist1-v3.docx
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Tracing the path of 37,050 studies into practice across 18 specialties of the 2.4 million published between 2011 and 2020
eLife 12:e82498.
https://doi.org/10.7554/eLife.82498
