Tuesday, January 20, 2015

Two New Papers

O’Mara-Eves A, Thomas J, McNaught J, Miwa M, Ananiadou S. Using text mining for study identification in systematic reviews: a systematic review of current approaches. Systematic Reviews 2015, 4:5  doi:10.1186/2046-4053-4-5

Background: The large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming. Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved. The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic review fills that research gap. Focusing mainly on non-technical issues, the review aims to increase awareness of the potential of these technologies and promote further collaborative research between the computer science and systematic review communities.

Methods: Five research questions led our review: what is the state of the evidence base; how has workload reduction been evaluated; what are the purposes of semi-automation and how effective are they; how have key contextual problems of applying text mining to the systematic review field been addressed; and what challenges to implementation have emerged?
We answered these questions using standard systematic review methods: systematic and exhaustive searching, quality-assured data extraction and a narrative synthesis to synthesise findings.

Results: The evidence base is active and diverse; there is almost no replication between studies or collaboration between research teams and, whilst it is difficult to establish any overall conclusions about best approaches, it is clear that efficiencies and reductions in workload are potentially achievable.
On the whole, most suggested that a saving in workload of between 30% and 70% might be possible, though sometimes the saving in workload is accompanied by the loss of 5% of relevant studies (i.e. a 95% recall).

Conclusions: Using text mining to prioritise the order in which items are screened should be considered safe and ready for use in 'live' reviews. The use of text mining as a 'second screener' may also be used cautiously. The use of text mining to eliminate studies automatically should be considered promising, but not yet fully proven. In highly technical/clinical areas, it may be used with a high degree of confidence; but more developmental and evaluative work is needed in other disciplines.

Finn Y, Cantillon P, Flaherty G. Exploration of a possible relationship between examiner stringency and personality factors in clinical assessments: a pilot study. BMC Medical Education 2014, 14:1052  doi:10.1186/s12909-014-0280-3

Background: The reliability of clinical examinations is known to vary considerably. Inter-examiner variability is a key source of this variability. Some examiners consistently give lower scores to some candidates compared to other examiners and vice versa – the ‘hawk- dove’ effect. Stable examiner characteristics, such as personality factors, may influence examiner stringency. We investigated whether examiner stringency is related to personality factors.

Methods: We recruited 12 examiners to view and score a video-recorded five station OSCE of six Year 1 undergraduate medical students at our institution. In addition examiners completed a validated personality questionnaire. Examiners’ markings were tested for statistically significant differences using non-parametric one way analysis of variance. The relationship between examiners’ markings and examiner personality factors was investigated using Spearman correlation coefficient.
Results: At each station there was a statistically significant difference between examiners markings, confirming the presence of inter-examiner variability. Correlation analysis showed no association between stringency and any of the five major personality factors. When we omitted an outlier examiner we found a statistically significant negative correlation between examiner stringency and openness to experience with a correlation coefficients (rho) of – 0.66 (p = 0.03). Conversely there was a moderate positive correlation between examiner stringency and neuroticism with a correlation coefficient (rho) of 0.73 (p = 0.01).

Conclusions: In this study we did not find any relationship between examiner stringency and examiner personality factors. However, following the elimination of an outlier examiner from the analysis, we found a significant relationship between examiner stringency and two of the big five personality factors (neuroticism and openness to experience). The significance of this outlier is not known. As this was a small pilot study we recommend further studies in this field to investigate if there is a relationship between examiner stringency in clinical assessments and personality factors.


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