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BMJ Open Oct 2017We audited a selection of systematic reviews published in 2013 and reported on the proportion of reviews that researched for unpublished data, included unpublished data... (Review)
Review
OBJECTIVES
We audited a selection of systematic reviews published in 2013 and reported on the proportion of reviews that researched for unpublished data, included unpublished data in analysis and assessed for publication bias.
DESIGN
Audit of systematic reviews.
DATA SOURCES
We searched PubMed and Ovid MEDLINE In-Process & Other Non-Indexed Citations between 1 January 2013 and 31 December 2013 for the following journals: , , , and the . We also searched the Cochrane Library and included 100 randomly selected Cochrane reviews.
ELIGIBILITY CRITERIA
Systematic reviews published in 2013 in the selected journals were included. Methodological reviews were excluded.
DATA EXTRACTION AND SYNTHESIS
Two reviewers independently reviewed each included systematic review. The following data were extracted: whether the review searched for grey literature or unpublished data, the sources searched, whether unpublished data were included in analysis, whether publication bias was assessed and whether there was evidence of publication bias.
MAIN FINDINGS
203 reviews were included for analysis. 36% (73/203) of studies did not describe any attempt to obtain unpublished studies or to search grey literature. 89% (116/130) of studies that sought unpublished data found them. 33% (68/203) of studies included an assessment of publication bias, and 40% (27/68) of these found evidence of publication bias.
CONCLUSION
A significant fraction of systematic reviews included in our study did not search for unpublished data. Publication bias may be present in almost half the published systematic reviews that assessed for it. Exclusion of unpublished data may lead to biased estimates of efficacy or safety in systematic reviews.
Topics: Bibliometrics; Data Collection; Humans; Publication Bias; Research Design; Review Literature as Topic
PubMed: 28988181
DOI: 10.1136/bmjopen-2017-017737 -
Journal of the American Medical... Jul 2022We aim to investigate the application and accuracy of artificial intelligence (AI) methods for automated medical literature screening for systematic reviews. (Meta-Analysis)
Meta-Analysis
OBJECTIVE
We aim to investigate the application and accuracy of artificial intelligence (AI) methods for automated medical literature screening for systematic reviews.
MATERIALS AND METHODS
We systematically searched PubMed, Embase, and IEEE Xplore Digital Library to identify potentially relevant studies. We included studies in automated literature screening that reported study question, source of dataset, and developed algorithm models for literature screening. The literature screening results by human investigators were considered to be the reference standard. Quantitative synthesis of the accuracy was conducted using a bivariate model.
RESULTS
Eighty-six studies were included in our systematic review and 17 studies were further included for meta-analysis. The combined recall, specificity, and precision were 0.928 [95% confidence interval (CI), 0.878-0.958], 0.647 (95% CI, 0.442-0.809), and 0.200 (95% CI, 0.135-0.287) when achieving maximized recall, but were 0.708 (95% CI, 0.570-0.816), 0.921 (95% CI, 0.824-0.967), and 0.461 (95% CI, 0.375-0.549) when achieving maximized precision in the AI models. No significant difference was found in recall among subgroup analyses including the algorithms, the number of screened literatures, and the fraction of included literatures.
DISCUSSION AND CONCLUSION
This systematic review and meta-analysis study showed that the recall is more important than the specificity or precision in literature screening, and a recall over 0.95 should be prioritized. We recommend to report the effectiveness indices of automatic algorithms separately. At the current stage manual literature screening is still indispensable for medical systematic reviews.
Topics: Algorithms; Artificial Intelligence; Humans; Mass Screening; Publications
PubMed: 35641139
DOI: 10.1093/jamia/ocac066 -
Journal of Oral Rehabilitation Mar 2021Knowledge about the magnitude of Oral Health-Related Quality of Life (OHRQoL) impairment across dental patient populations is essential for clinical practice, public... (Review)
Review
Dental patients' functional, pain-related, aesthetic, and psychosocial impact of oral conditions on quality of life-Project overview, data collection, quality assessment, and publication bias.
BACKGROUND
Knowledge about the magnitude of Oral Health-Related Quality of Life (OHRQoL) impairment across dental patient populations is essential for clinical practice, public health and research. Within the project Mapping Oral Disease Impact with a Common Metric, this systematic review aimed to describe functional, pain-related, aesthetic and broader psychosocial impact of oral conditions with a single metric using OHRQoL dimensions Oral Function, Oro facial Pain, Oro facial Appearance and Psychosocial Impact.
METHODS
A search using PubMed, EMBASE, Cochrane, CINAHL and PsycINFO was performed on 8 June 2017, and updated on 14 January 2019. Only publications in the English language were considered. To characterise the extent of available standardised and clinically relevant OHRQoL information, we determined the number of publications, dental patient populations, which are clinically similar, and patient samples within each population with four-dimensional OHRQoL information using the Oral Health Impact Profile (OHIP) questionnaire. A quality assessment and a publication bias assessment were performed.
RESULTS
We identified 171 publications that characterised 199 dental populations and 329 patient samples with four-dimensional OHRQoL information. The vast majority of populations were only characterised by one patient sample. Study quality was not related to OHRQoL magnitude, and substantial publication bias could be excluded.
CONCLUSIONS
Standardised and clinically relevant information using the four OHRQoL dimensions Oral Function, Oro facial Pain, Oro facial Appearance and Psychosocial Impact was available for a significant number of dental patient populations. Findings can provide a framework to interpret OHRQoL impairment of individual patients, or groups of patients, for clinical practice, public health and research.
Topics: Esthetics, Dental; Humans; Oral Health; Publication Bias; Quality of Life; Surveys and Questionnaires
PubMed: 32628288
DOI: 10.1111/joor.13045 -
Family Medicine and Community Health Nov 2022Artificial intelligence (AI) will have a significant impact on healthcare over the coming decade. At the same time, health inequity remains one of the biggest...
OBJECTIVE
Artificial intelligence (AI) will have a significant impact on healthcare over the coming decade. At the same time, health inequity remains one of the biggest challenges. Primary care is both a driver and a mitigator of health inequities and with AI gaining traction in primary care, there is a need for a holistic understanding of how AI affect health inequities, through the act of providing care and through potential system effects. This paper presents a systematic scoping review of the ways AI implementation in primary care may impact health inequity.
DESIGN
Following a systematic scoping review approach, we searched for literature related to AI, health inequity, and implementation challenges of AI in primary care. In addition, articles from primary exploratory searches were added, and through reference screening.The results were thematically summarised and used to produce both a narrative and conceptual model for the mechanisms by which social determinants of health and AI in primary care could interact to either improve or worsen health inequities.Two public advisors were involved in the review process.
ELIGIBILITY CRITERIA
Peer-reviewed publications and grey literature in English and Scandinavian languages.
INFORMATION SOURCES
PubMed, SCOPUS and JSTOR.
RESULTS
A total of 1529 publications were identified, of which 86 met the inclusion criteria. The findings were summarised under six different domains, covering both positive and negative effects: (1) access, (2) trust, (3) dehumanisation, (4) agency for self-care, (5) algorithmic bias and (6) external effects. The five first domains cover aspects of the interface between the patient and the primary care system, while the last domain covers care system-wide and societal effects of AI in primary care. A graphical model has been produced to illustrate this. Community involvement throughout the whole process of designing and implementing of AI in primary care was a common suggestion to mitigate the potential negative effects of AI.
CONCLUSION
AI has the potential to affect health inequities through a multitude of ways, both directly in the patient consultation and through transformative system effects. This review summarises these effects from a system tive and provides a base for future research into responsible implementation.
Topics: Humans; Artificial Intelligence; Health Inequities; Gray Literature; PubMed; Primary Health Care
PubMed: 36450391
DOI: 10.1136/fmch-2022-001670 -
BMJ Open Oct 2023Prospectively registering study plans in a permanent time-stamped and publicly accessible document is becoming more common across disciplines and aims to reduce risk of... (Meta-Analysis)
Meta-Analysis
OBJECTIVES
Prospectively registering study plans in a permanent time-stamped and publicly accessible document is becoming more common across disciplines and aims to reduce risk of bias and make risk of bias transparent. Selective reporting persists, however, when researchers deviate from their registered plans without disclosure. This systematic review aimed to estimate the prevalence of undisclosed discrepancies between prospectively registered study plans and their associated publication. We further aimed to identify the research disciplines where these discrepancies have been observed, whether interventions to reduce discrepancies have been conducted, and gaps in the literature.
DESIGN
Systematic review and meta-analyses.
DATA SOURCES
Scopus and Web of Knowledge, published up to 15 December 2019.
ELIGIBILITY CRITERIA
Articles that included quantitative data about discrepancies between registrations or study protocols and their associated publications.
DATA EXTRACTION AND SYNTHESIS
Each included article was independently coded by two reviewers using a coding form designed for this review (osf.io/728ys). We used random-effects meta-analyses to synthesise the results.
RESULTS
We reviewed k=89 articles, which included k=70 that reported on primary outcome discrepancies from n=6314 studies and, k=22 that reported on secondary outcome discrepancies from n=1436 studies. Meta-analyses indicated that between 29% and 37% (95% CI) of studies contained at least one primary outcome discrepancy and between 50% and 75% (95% CI) contained at least one secondary outcome discrepancy. Almost all articles assessed clinical literature, and there was considerable heterogeneity. We identified only one article that attempted to correct discrepancies.
CONCLUSIONS
Many articles did not include information on whether discrepancies were disclosed, which version of a registration they compared publications to and whether the registration was prospective. Thus, our estimates represent discrepancies broadly, rather than our target of discrepancies between registered study plans and their associated publications. Discrepancies are common and reduce the trustworthiness of medical research. Interventions to reduce discrepancies could prove valuable.
REGISTRATION
osf.io/ktmdg. Protocol amendments are listed in online supplemental material A.
Topics: Humans; Prospective Studies; Prevalence; Bias; Publication Bias; Biomedical Research
PubMed: 37793922
DOI: 10.1136/bmjopen-2023-076264 -
BMJ Open Mar 2018To determine whether methodological and reporting quality are associated with surrogate measures of publication impact in the field of dementia biomarker studies. (Review)
Review
Are methodological quality and completeness of reporting associated with citation-based measures of publication impact? A secondary analysis of a systematic review of dementia biomarker studies.
OBJECTIVE
To determine whether methodological and reporting quality are associated with surrogate measures of publication impact in the field of dementia biomarker studies.
METHODS
We assessed dementia biomarker studies included in a previous systematic review in terms of methodological and reporting quality using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) and Standards for Reporting of Diagnostic Accuracy (STARD), respectively. We extracted additional study and journal-related data from each publication to account for factors shown to be associated with impact in previous research. We explored associations between potential determinants and measures of publication impact in univariable and stepwise multivariable linear regression analyses.
OUTCOME MEASURES
We aimed to collect data on four measures of publication impact: two traditional measures-average number of citations per year and 5-year impact factor of the publishing journal and two alternative measures-the Altmetric Attention Score and counts of electronic downloads.
RESULTS
The systematic review included 142 studies. Due to limited data, Altmetric Attention Scores and electronic downloads were excluded from the analysis, leaving traditional metrics as the only analysed outcome measures. We found no relationship between QUADAS and traditional metrics. Citation rates were independently associated with 5-year journal impact factor (β=0.42; p<0.001), journal subject area (β=0.39; p<0.001), number of years since publication (β=-0.29; p<0.001) and STARD (β=0.13; p<0.05). Independent determinants of 5-year journal impact factor were citation rates (β=0.45; p<0.001), statement on conflict of interest (β=0.22; p<0.01) and baseline sample size (β=0.15; p<0.05).
CONCLUSIONS
Citation rates and 5-year journal impact factor appear to measure different dimensions of impact. Citation rates were weakly associated with completeness of reporting, while neither traditional metric was related to methodological rigour. Our results suggest that high publication usage and journal outlet is not a guarantee of quality and readers should critically appraise all papers regardless of presumed impact.
Topics: Bibliometrics; Biomarkers; Conflict of Interest; Dementia; Humans; Journal Impact Factor; Periodicals as Topic; Sample Size
PubMed: 29572396
DOI: 10.1136/bmjopen-2017-020331 -
BMC Health Services Research Nov 2014When the nature and direction of research results affect their chances of publication, a distortion of the evidence base - termed publication bias - results. Despite... (Review)
Review
BACKGROUND
When the nature and direction of research results affect their chances of publication, a distortion of the evidence base - termed publication bias - results. Despite considerable recent efforts to implement measures to reduce the non-publication of trials, publication bias is still a major problem in medical research. The objective of our study was to identify barriers to and facilitators of interventions to prevent or reduce publication bias.
METHODS
We systematically reviewed the scholarly literature and extracted data from articles. Further, we performed semi-structured interviews with stakeholders. We performed an inductive thematic analysis to identify barriers to and facilitators of interventions to counter publication bias.
RESULTS
The systematic review identified 39 articles. Thirty-four of 89 invited interview partners agreed to be interviewed. We clustered interventions into four categories: prospective trial registration, incentives for reporting in peer-reviewed journals or research reports, public availability of individual patient-level data, and peer-review/editorial processes. Barriers we identified included economic and personal interests, lack of financial resources for a global comprehensive trial registry, and different legal systems. Facilitators identified included: raising awareness of the effects of publication bias, providing incentives to make data publically available, and implementing laws to enforce prospective registration and reporting of clinical trial results.
CONCLUSIONS
Publication bias is a complex problem that reflects the complex system in which it occurs. The cooperation amongst stakeholders to increase public awareness of the problem, better tailoring of incentives to publish, and ultimately legislative regulations have the greatest potential for reducing publication bias.
Topics: Biomedical Research; Humans; Peer Review; Prospective Studies; Publication Bias; Publishing; Registries; Research Report; Surveys and Questionnaires; United Kingdom
PubMed: 25719959
DOI: 10.1186/s12913-014-0551-z -
Physical and Engineering Sciences in... Mar 2022To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of... (Review)
Review
OBJECTIVES
To conduct a systematic survey of published techniques for automated diagnosis and prognosis of COVID-19 diseases using medical imaging, assessing the validity of reported performance and investigating the proposed clinical use-case. To conduct a scoping review into the authors publishing such work.
METHODS
The Scopus database was queried and studies were screened for article type, and minimum source normalized impact per paper and citations, before manual relevance assessment and a bias assessment derived from a subset of the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). The number of failures of the full CLAIM was adopted as a surrogate for risk-of-bias. Methodological and performance measurements were collected from each technique. Each study was assessed by one author. Comparisons were evaluated for significance with a two-sided independent t-test.
FINDINGS
Of 1002 studies identified, 390 remained after screening and 81 after relevance and bias exclusion. The ratio of exclusion for bias was 71%, indicative of a high level of bias in the field. The mean number of CLAIM failures per study was 8.3 ± 3.9 [1,17] (mean ± standard deviation [min,max]). 58% of methods performed diagnosis versus 31% prognosis. Of the diagnostic methods, 38% differentiated COVID-19 from healthy controls. For diagnostic techniques, area under the receiver operating curve (AUC) = 0.924 ± 0.074 [0.810,0.991] and accuracy = 91.7% ± 6.4 [79.0,99.0]. For prognostic techniques, AUC = 0.836 ± 0.126 [0.605,0.980] and accuracy = 78.4% ± 9.4 [62.5,98.0]. CLAIM failures did not correlate with performance, providing confidence that the highest results were not driven by biased papers. Deep learning techniques reported higher AUC (p < 0.05) and accuracy (p < 0.05), but no difference in CLAIM failures was identified.
INTERPRETATION
A majority of papers focus on the less clinically impactful diagnosis task, contrasted with prognosis, with a significant portion performing a clinically unnecessary task of differentiating COVID-19 from healthy. Authors should consider the clinical scenario in which their work would be deployed when developing techniques. Nevertheless, studies report superb performance in a potentially impactful application. Future work is warranted in translating techniques into clinical tools.
Topics: Artificial Intelligence; COVID-19; COVID-19 Testing; Humans; Publishing; Radiography; SARS-CoV-2
PubMed: 34919204
DOI: 10.1007/s13246-021-01093-0 -
International Journal of Surgery... Jun 2024Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better...
AIM
Computer-aided drug design (CADD) is a drug design technique for computing ligand-receptor interactions and is involved in various stages of drug development. To better grasp the frontiers and hotspots of CADD, we conducted a review analysis through bibliometrics.
METHODS
A systematic review of studies published between 2000 and 20 July 2023 was conducted following the PRISMA guidelines. Literature on CADD was selected from the Web of Science Core Collection. General information, publications, output trends, countries/regions, institutions, journals, keywords, and influential authors were visually analyzed using software such as Excel, VOSviewer, RStudio, and CiteSpace.
RESULTS
A total of 2031 publications were included. These publications primarily originated from 99 countries or regions led by the U.S. and China. Among the contributors, MacKerell AD had the highest number of articles and the greatest influence. The Journal of Medicinal Chemistry was the most cited journal, whereas the Journal of Chemical Information and Modeling had the highest number of publications.
CONCLUSIONS
Influential authors in the field were identified. Current research shows active collaboration between countries, institutions, and companies. CADD technologies such as homology modeling, pharmacophore modeling, quantitative conformational relationships, molecular docking, molecular dynamics simulation, binding free energy prediction, and high-throughput virtual screening can effectively improve the efficiency of new drug discovery. Artificial intelligence-assisted drug design and screening based on CADD represent key topics that will influence future development. Furthermore, this paper will be helpful in better understanding the frontiers and hotspots of CADD.
Topics: Bibliometrics; Humans; Computer-Aided Design; Drug Design; Molecular Docking Simulation
PubMed: 38502850
DOI: 10.1097/JS9.0000000000001289 -
International Journal of Molecular... Nov 2022Recent evidence links chronic consumption of large amounts of fructose (FRU) with several non-communicable disease. After ingestion, dietary FRU is absorbed into the... (Review)
Review
Recent evidence links chronic consumption of large amounts of fructose (FRU) with several non-communicable disease. After ingestion, dietary FRU is absorbed into the intestinal tract by glucose transporter (GLUT) 5 and transported to the portal vein via GLUT2. GLUT2 is primarily localized on the basolateral membrane, but GLUT2 may be dislocated post-prandially from the basolateral membrane of intestinal cells to the apical one. Polyphenols (PP) are plant secondary metabolites that exert hypoglycemic properties by modulating intracellular insulin signaling pathways and by inhibiting intestinal enzymes and transporters. Post-prandially, PP may reach high concentrations in the gut lumen, making the inhibition of FRU absorption a prime target for exploring the effects of PP on FRU metabolism. Herein, we have systematically reviewed studies on the effect of PP and PP-rich products on FRU uptake and transport in intestinal cells. In spite of expectations, the very different experimental conditions in the various individual studies do not allow definitive conclusions to be drawn. Future investigations should rely on standardized conditions in order to obtain comparable results that allow a credible rating of polyphenols and polyphenol-rich products as inhibitors of fructose uptake.
Topics: Polyphenols; Biological Transport; Intestines; Publications; Fructose
PubMed: 36430831
DOI: 10.3390/ijms232214355