Error rates of human reviewers during abstract screening in systematic reviews.

Background: Automated approaches to improve the efficiency of systematic reviews are greatly needed. When testing any of these approaches, the criterion standard of comparison (gold standard) is usually human reviewers. Yet, human reviewers make errors in inclusion and exclusion of references.

Exploiting the systematic review protocol for classification of medical abstracts.

Objective: To determine whether the automatic classification of documents can be useful in systematic reviews on medical topics, and specifically if the performance of the automatic classification can be enhanced by using the particular protocol of questions employed by the human reviewers to create multiple classifiers.

Methods And Materials: The test collection is the data used in large-scale systematic review on the topic of the dissemination strategy of health care services for elderly people. From a group of 47,274 abstracts marked by human reviewers to be included in or excluded from further screening, we randomly selected 20,000 as a training set, with the remaining 27,274 becoming a separate test set.

A new algorithm for reducing the workload of experts in performing systematic reviews.

Objective: To determine whether a factorized version of the complement naïve Bayes (FCNB) classifier can reduce the time spent by experts reviewing journal articles for inclusion in systematic reviews of drug class efficacy for disease treatment.

Design: The proposed classifier was evaluated on a test collection built from 15 systematic drug class reviews used in previous work. The FCNB classifier was constructed to classify each article as containing high-quality, drug class-specific evidence or not.

Many quality measurements, but few quality measures assessing the quality of breast cancer care in women: a systematic review.

Background: Breast cancer in women is increasingly frequent, and care is complex, onerous and expensive, all of which lend urgency to improvements in care. Quality measurement is essential to monitor effectiveness and to guide improvements in healthcare.

Methods: Ten databases, including Medline, were searched electronically to identify measures assessing the quality of breast cancer care in women (diagnosis, treatment, followup, documentation of care).