The automation of systematic reviews
BMJ 2013; 346 doi: https://doi.org/10.1136/bmj.f139 (Published 10 January 2013) Cite this as: BMJ 2013;346:f139
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Cochrane reviews are conducted in accordance with peer-reviewed published protocols. This is a major determinant of the quality of reviews published in The Cochrane Library. Published protocols serve as a public record of the pre-specified design of a systematic review and reduce the possibility for bias by preventing review authors from making post hoc decisions unless they are explicitly described and justified within the review.
In a timely editorial exploring ways that the systematic reviewing process may be automated, Tsafnat et al dream of being able to create, edit and execute protocols at the push of a button.[1] We may be a couple of steps away from full automation, but we at the editorial office Cochrane Airways Group (a Cochrane Review Group of the Cochrane Collaboration) have been thinking about ways in which we can standardise our approach and have identified a method to effectively automate parts of the production of the Cochrane protocol thus “freeing the reviewer to focus on developing and validating the review question and protocol”. We believe this process will streamline protocol production without compromising the quality of our product.
The Cochrane Collaboration recently agreed on and published a set of methodological expectations for Cochrane Reviews: the Methodological Expectations of Cochrane Intervention Reviews (MECIR) project.[2] We saw an opportunity to develop a standard template protocol based on these expectations. We call this a ‘prepared protocol’.
The prepared protocol consists of three types of text. The first type of text should be read and understood by the author but mainly left unaltered. This text comprises the minimum standards we would expect from the majority of reviews (i.e. boiler plate text), and adheres to the Cochrane Style Guide sparing time and effort during the editorial process. For example “We will include randomised controlled trials (RCTs). We will include studies reported as full-text, those published as abstract only, and unpublished trials/data.” There is also text to describe what action must be taken if the author wishes to deviate from that standard text: [You must include RCTs in your review, but if you want to include any additional study designs, you must justify the choice of eligible study deigns]. There is also text to delete where appropriate: “We will use a (*delete as appropriate: random-effects/fixed-effect) model and perform a sensitivity analysis with (*the other random/fixed model).”
The prepared protocol text provides the author with the bulk of the text needed to describe the methodology, but leaves them free to add additional text to describe methods that they need to answer their particular review question. We also provide appendices to describe standard search strategies and suitable references for the review author. Authors must provide their own text for the background section and objectives, as well as pre-specify primary and secondary outcomes, outcomes for use in a summary of findings table and approaches to subgroup and sensitivity analyses. We believe that this approach enables authors to focus on tailoring the protocol to suit their question, whilst providing some assurance that they are not engaging in a box-ticking exercise.
Time-saving advantages are not unique to the author team and we have also begun using Cochrane’s own internal IT infrastructure to supplement this process. Because the prepared protocol text is inserted into the software file in which the Cochrane Review will be written[3], differences between versions can be identified by editorial staff by comparing them on the Cochrane Collaboration server, Archie. This means that editorial staff can easily identify to those parts that have changed.
We believe this approach will save both review author and editorial staff time and free us to focus on the best way to design and implement methods that will address important review questions rather than drafting and editing methods on which the Collaboration as a whole has already agreed.
e-mail: ewelsh@sgul.ac.uk
References
1. Tsafnat G, Dunn A, Glasziou P, Coiera E. The automation of systematic reviews, BMJ 2013;346:f139
2. Churchill R, Higgins J, Chandler J, Tovey D, Lasserson T. Methodological Expectations of Cochrane Intervention Reviews (MECIR), http://www.editorial-unit.cochrane.org/mecir
3 Review Manager (RevMan) [Computer program]. Version 5.2. Copenhagen: The Nordic Cochrane Centre, The Cochrane Collaboration, 2012 (http://ims.cochrane.org/revman)
Competing interests: I am employed by SGUL as the Managing Editor of the Cochrane Airways Group and have authored several Cochrane Reviews.
We welcome the editorial by Tsafnat and colleagues, which is a timely statement of the potential for new information technologies to increase systematic review efficiency, and support the Rapid Responses by Barreto and Elliott et al. However, while the potential for automation is apparent, further methodological and evaluative work is needed to develop methods and an evidence base for their use; in particular, the amount of empirical data to support many of the purposes outlined in the editorial is very limited. [1] Moreover, there are likely to be some situations in which such procedures operate better than others. For example, natural language processing may work well in highly technical literatures that contain considerable jargon and regularly employed acronyms, but operate less effectively in non-clinical literature, such as public health, where the same meaning can be expressed in multiple ways. With this in mind, developing fit for purpose tools can be best achieved through user engagement and cross-disciplinary collaboration between computer scientists, information specialists, and systematic reviewers.
We currently hold an MRC grant that is aiming to develop methods for using text mining for use in systematic reviews – and to evaluate the potential of such technologies to increase the efficiency of screening studies for eligibility. Part of the work involves simulations on existing review data, and part on using text mining in current reviews. We would like to encourage reviewers to contact us if they have data that we could use; might like to participate in a ‘live’ review; or would like to hear more about the study.[2]
E-mail: j.thomas@ioe.ac.uk
[1] Thomas J, McNaught J, Ananiadou S (2011) Applications of text mining within systematic reviews. Research Synthesis Methods. 2(1): 1-14
Competing interests: Some of the above are authors of the paper cited and investigators on the project mentioned.
I read with interest the Editorial by Tsafnat et al.1 The idea of a complete automation of systematic reviews is attractive but hardly sustainable. Whilst some degree of automation would be welcome, aspects involved in the elaboration of a review article ask necessarily for the human work. For example, the reporting of original studies does not always completely or precisely describe all the methodological aspects employed by the researchers, which can be particularly problematic for defining if a study met the inclusion criteria of a review and for the evaluation of the risk of bias; that is why authors of reviews often need to contact the authors of the original studies to clarify aspects that were not clearly reported in the published paper.
Moreover, Tsafnat et al.1 highlight that there are four basic tasks that underpin systematic review and that “technology can help in each”. However, they forgot the most important aspect of a review, i.e., to give meaning to the evidence synthesised. Therefore, interpreting data is the most crucial aspect in a review; human work at the interpretative stage of a review is irreplaceable.
In conclusion, whilst automation can positively impact the way systematic reviews are performed, by transforming updating in a “immediate, and universal” process, creation and update of reviews will never be “effortless”. And we have all the interest that it continues effortful.
e-mail: philipebarreto81@yahoo.com.br
References
1. Tsafnat G, , Dunn A, Glasziou P, Coiera E. The automation of systematic reviews. BMJ 2013;346:f139
Competing interests: No competing interests
We would like to congratulate Tsafnat and colleagues for a rich overview of automation in systematic review production [1]. We support the authors’ central argument that automation has the potential to transform the processes involved in producing systematic reviews. Indeed, technological innovation has played a central role since the birth of systematic review [2] and machine processes are embedded throughout current systematic review workflow from evidence retrieval to meta-analysis. Nevertheless, significant inefficiency and redundancy remain and current methods are not sustainable in the face of expanding demands for high quality evidence and the data deluge of primary research [3].
Tsafnat and colleagues highlight the substantial innovation that has occurred in this field in recent years, yet there has been a dearth of real world applications implemented and widely available for review authors. A key challenge has been achieving performance perceived to be adequate by users who often prioritise methodological rigor over efficiency. Furthermore, many promising innovations are yet to develop into viable services and relevant advances in related fields have not been translated into applications for systematic review. We need vibrant environments for systematic review innovation and incentive structures for rapid and broad release.
Whilst a systematic review remains dependent on the analysis of unstructured data and text, human input is likely to remain critical at every step of review workflow. We should therefore focus not just on ‘making the machines work harder’, but in creating the best partnership between people and machines [4]. For example, the Cochrane Collaboration is working to optimise the value of human effort by utilising the PICO structure to link reports, studies, reviews and external data sources in linked data repositories based on semantic technologies [5]. The relationship between the new Cochrane Register of Studies and Cochrane’s review writing software, RevMan, continues to develop towards a vision of semi-automated inclusion of extracted data into reviews from a common data repository.
Achieving efficient production of high quality evidence reviews is an important public good. With ongoing and diverse innovations, such as those described by Tsafnat and colleagues, we believe the trade-offs in evidence synthesis between methodological rigour and review currency can be eroded, resulting ultimately in ‘living systematic reviews’: high quality online evidence summaries that are dynamically updated as new evidence becomes available [6].
1. Tsafnat G, Dunn A, Glasziou P, Coiera E. The automation of systematic reviews. BMJ 2013;346:f139.
2. Chalmers I. Electronic publications for updating controlled trial reviews. Lancet 1986;328:287.
3. Bastian H, Glaziou P, Chalmers I. Seventy-five trials and eleven systematic reviews a day: how we will we ever keep up? PLoS Med 7(9):e1000326.
4. Kasparov G. The chess master and the computer. The New York Review of Books February 11, 2010. http://www.nybooks.com/articles/archives/2010/feb/11/the-chess-master-an...
5. Mavergames C. Becker L. Cochrane Linked Data Project: From “Star Trek” to the present. December 2012. http://www.cochrane.org/community/development-projects/cochrane-linked-d...
6. Elliott J. Exploiting innovations in technology to improve the efficiency of review production. 20th Cochrane Colloquium, Auckland, October 2012. http://webcast.gigtv.com.au/Mediasite/Catalog/catalogs/colloquium/?state...
Competing interests: All authors are either employed by or are active contributors to the Cochrane Collaboration.
Re: The automation of systematic reviews
Optimism is important but reality matters most. We agree with this editorial (1) : comprehensive, complete and up to date systematic reviews are desirable. However, automating systematic reviews to do this is a utopian aim that is flawed in its dismissal of language, its scientism, and a mis-placed faith in technology.(2)
That the authors describe systematic reviews as involving ‘basic tasks’ of retrieval, appraisal, synthesis and publication lays bear simplistic misconceptions. Language in systematic review is inescapable and necessarily involves interpretation.(3) For example, descriptions of health interventions are often messy, inconsistent, and incomplete. These descriptions themselves are interpreted when re-described, classified or quantified.(4) Appraisal of bias in studies, though protocol-led, still involves all manner of interpretations and a fusion of judgment, nuance and fairness that computers cannot provide. Finally, conclusions are never inherent in pooled data: do null pooled findings indicate there is ‘no evidence to support an intervention’ or should this intervention ‘not be practiced?’(5) Arguments that these difficulties can be addressed via superior technology, better or more rigorous methods and / or the removal of humans altogether are predictable and amiss. The belief that method and technology can conquer the challenges created by language, as the authors do, is historically common but wrong.(6)
Humans should always be involved in systematic reviews. While ‘technology’ can assist them identify relevant studies, classify these using taxonomies, appraise their quality via tools and calculate synthesized results software – what studies, data and conclusions mean is always about interpretation and needs to involve humans. To do so is not to admit failure in method or science or technology but to recognize properly the nature of language and reality.
References
1 Tsafnat G, Dunn A, Glasziou P, Coiera E. The automation of systematic reviewsWould lead to best currently available evidence at the push of a button. BMJ. 2013;346(3): f139. doi:10.1136/bmj.f139.
2 Postman N. Technopoly: The surrender of culture to technology. Londong: Vintage; 1993.
3 Palmer R. Hermeneutics: Interpretation Theory in Schleiermacher, Dilthey, Heidegger, and Gadamer. Evanston: Northwestern University Press; 1969.
4 Pawson R. Evidence-based policy: A realist perspective. London: Sage; 2006.
5 Haykowsky M, Liang Y, Pechter D, Jones L, McAlister F, Clark AM. A Meta-Analysis of the Effect of Exercise Training on Left Ventricular Remodeling in Heart Failure Patients: The Benefit Depends on the Type of Training Performed. J Am Coll Cardiol. 2007;49:2329-36.
6 Gadamer HG. Truth and Method. 2 Revised ed. London: Sheed and Ward; 1989.
Competing interests: No competing interests