It was not easy to identify the study design from the title and abstract of articles indexed as diagnostic (test) accuracy studies in EMBASE in 2012 and 2019

Thompson, Georgina; Zhelev, Zhivko; Hunt, Harriet; Hyde, Chris

doi:10.1016/j.jclinepi.2021.12.014

Cited by 1 publication

(2 citation statements)

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“…However, for DTA studies, the indexing term is only available in Embase, and it is known to be inadequate. 12 Due to these reasons, none of the current abstract classifiers for identifying primary DTA studies have a sufficient combination of high recall with reasonable precision required for systematic reviews. This absence necessitates a dedicated classifier to minimize the number of abstracts to read when conducting a systematic database search for new DTA systematic reviews.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, indexing terms for RCTs are available in both MEDLINE and Embase. However, for DTA studies, the indexing term is only available in Embase, and it is known to be inadequate 12 . Due to these reasons, none of the current abstract classifiers for identifying primary DTA studies have a sufficient combination of high recall with reasonable precision required for systematic reviews.…”

Section: Introductionmentioning

confidence: 99%

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An open competition involving thousands of competitors failed to construct useful abstract classifiers for new diagnostic test accuracy systematic reviews

Kataoka

Taito

Yamamoto

et al. 2023

Research Synthesis Methods

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%