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There are currently no abstract classifiers, which can be used for new diagnostic test accuracy (DTA) systematic reviews to select primary DTA study abstracts from database searches. Our goal was to develop machine-learning-based abstract classifiers for new DTA systematic reviews through an open competition. We prepared a dataset of abstracts obtained through database searches from 11 reviews in different clinical areas. As the reference standard, we used the abstract lists that required manual full-text review. We randomly splitted the datasets into a train set, a public test set, and a private test set. Competition participants used the training set to develop classifiers and validated their classifiers using the public test set. The classifiers were refined based on the performance of the public test set. They could submit as many times as they wanted during the competition. Finally, we used the private test set to rank the submitted classifiers. To reduce false exclusions, we used the Fbeta measure with a beta set to seven for evaluating classifiers. After the competition, we conducted the external validation using a dataset from a cardiology DTA review. We received 13,774 submissions from 1429 teams or persons over 4 months. The top-honored classifier achieved a Fbeta score of 0.4036 and a recall of 0.2352 in the external validation. In conclusion, we were unable to develop an abstract classifier with sufficient recall for immediate application to new DTA systematic reviews. Further studies are needed to update and validate classifiers with datasets from other clinical areas.

BACKGROUND: Spontaneous remission is common in patients with undiagnosed classic fever of unknown origin (FUO). Although identifying reliable predictors of spontaneous remission in such diagnostically challenging cases could improve their management strategies, few studies have assessed such clinical factors. Recently, studies have reported that F-fluorodeoxyglucose positron emission tomography (F-FDG PET) alone and integrated with computed tomography (PET/CT) were useful in localizing the source of FUO. In this systematic review and meta-analysis, we assessed the association of results of these imaging modalities with spontaneous remission in patients with classic FUO. METHODS: We searched PubMed and Scopus from inception until June 30, 2018, and studies that evaluated the PET or PET/CT results of ≥10 adult or adolescent patients with classic FUO who were followed up for at least 3 months were included. At least 2 investigators extracted data and rated quality using the QUIPS-2 tool. We used a random-effects meta-analysis to calculate summary risk ratios (RRs) with 95% confidence intervals (CIs). RESULTS: Nine studies of PET/CT results (418 patients) and 4 studies of standalone PET results (128 patients) were eligible. None explicitly specified the incidence of spontaneous remission as the primary or secondary outcomes of interest. The risk of bias was considered high in all studies because patients received subsequent diagnostic workup based on imaging results. Patients with negative PET/CT results were significantly more likely to present with spontaneous regression than those with positive results (summary RR = 5.6; 95% CI: 3.4-9.2; P < .001; I = 0%). In contrast, no significant association was found between standalone PET results and spontaneous remission. The random-effects study-level meta-regression found that PET/CT results [relative RR (rRR) = 7.4; 95% CI: 2.5-21.3; P = .002], compared with standalone PET results, and publication year (rRR = 1.2 per 1 year; 95% CI: 1.0-1.3; P = .013) were significantly associated with spontaneous remission. CONCLUSION: Limited data suggest that undiagnosed classic FUO patients with negative PET/CT results had a high likelihood of spontaneous remission after a series of unsuccessful investigations for fever workup. Prospective studies should validate these results.

UNLABELLED: Several studies have assessed nuclear imaging tests for localizing the source of fever in patients with classic fever of unknown origin (FUO); however, the role of these tests in clinical practice remains unclear. We systematically reviewed the test performance, diagnostic yield, and management decision impact of nuclear imaging tests in patients with classic FUO. METHODS: We searched PubMed, Scopus, and other databases through October 31, 2015, to identify studies reporting on the diagnostic accuracy or impact on diagnosis and management decisions of 18F-FDG PET alone or integrated with CT (18F-FDG PET/CT), gallium scintigraphy, or leukocyte scintigraphy. Two reviewers extracted data. We quantitatively synthesized test performance and diagnostic yield and descriptively analyzed evidence about the impact on management decisions. RESULTS: We included 42 studies with 2,058 patients. Studies were heterogeneous and had methodologic limitations. Diagnostic yield was higher in studies with higher prevalence of neoplasms and infections. Nonneoplastic causes, such as adult-onset Still's disease and polymyalgia rheumatica, were less successfully localized. Indirect evidence suggested that 18F-FDG PET/CT had the best test performance and diagnostic yield among the 4 imaging tests; summary sensitivity was 0.86 (95% confidence interval [CI], 0.81-0.90), specificity 0.52 (95% CI, 0.36-0.67), and diagnostic yield 0.58 (95% CI, 0.51-0.64). Evidence on direct comparisons of alternative imaging modalities or on the impact of tests on management decisions was limited. CONCLUSION: Nuclear imaging tests, particularly 18F-FDG PET/CT, can be useful in identifying the source of fever in patients with classic FUO. The contribution of nuclear imaging may be limited in clinical settings in which infective and neoplastic causes are less common. Studies using standardized diagnostic algorithms are needed to determine the optimal timing for testing and to assess the impact of tests on management decisions and patient-relevant outcomes.