Development and validation of a search strategy and an automated classifier for retrieving temporomandibular disorders studies

Abstract

The objective was to develop and evaluate a comprehensive search strategy (SS) and automated classifier (AC) for retrieving temporomandibular disorders (TMD) research articles. An initial version of SS and AC was created by compiling terms from various sources, including previous systematic reviews (SRs) and consulting with TMD specialists. Performance was assessed using the relative recall (RR) method against a sample of all the primary studies (PS) included in 100 TMD-related SRs, with RR calculated for both SS and AC based on their ability to capture/classify TMD PSs. Adjustments were made iteratively. A validation was performed against PSs included in all TMD-relevant SRs published from January to April 2023. The analysis included 1271 PSs from 100 SRs published between 2002-2022. The initial SS had a relative recall of 89.34%, while the AC detected 70.05% of the studies. After adjustments, the fifth version reached 99.5% and 89.5% relative recall, respectively. Validation with 28 SRs from 2023 showed a search strategy sensitivity of 99.67% and AC sensitivity of 88.04%. In conclusion, the proposed SS demonstrated excellent performance in retrieving TMD-related research articles, with only a small percentage not correctly classified by the AC. The SS can effectively support evidence synthesis related to TMD, while the AC can aid in creating an open-access, continuously updated digital repository for all relevant TMD evidence.

Keywords: Automated classification; Evidence-based dentistry; Research methodology; Systematic review; Temporomandibular disorders.