CLEAR item#11
“Eligibility criteria. Define the inclusion criteria first. Then, specify the exclusion criteria. Avoid redundancies by using the opposite of the inclusion criteria as exclusion criteria. Specify the selection process (e.g., random, consecutive). Keep the numeric details of eligibility for the results.” [1] (from the article by Kocak et al.; licensed under CC BY 4.0)
Reporting examples for CLEAR item#11
Example#1. “The inclusion criteria were: (a) available baseline contrast-enhanced CT acquired up to two months before anti-PD-1/PD-L1 initiation; (b) presence of at least one measurable tumoral lesion according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 on the baseline CT; (c) presence of at least one follow-up CT after 2 to 4 cycles of immunotherapy to assess response. The exclusion criteria were: (a) baseline CT with slice thickness greater than 3mm; (b) patients presenting with ill-defined lesions only or poor CT quality; (c) patients who received intercurrent radiotherapy or surgery on target lesions; (d) non-progressive patients with follow-up time less than 6 months.” [37] (from the article by Cousin et al.; licensed under CC BY 4.0)
Example#2. “A total of 822 consecutive patients with AIS were chosen for inclusion. This study included patients with AIS who met the following criteria: (1) were undergoing IVT in accordance with the management guidelines for AIS, (2) had completed NCCT examination before IVT therapy, and (3) underwent a follow-up MRI or NCCT within 36 h after receiving IVT. Patients with head trauma injuries, primary cerebral hemorrhage or brain tumors, hemorrhagic infarction upon admission, insufficient data, and severe artifacts on NCCT images were excluded.” [38] (from the article by Ren et al.; licensed under CC BY 4.0)
Explanation and elaboration of CLEAR item#11
Ensuring the integrity and reproducibility of a study begins with the precise definition of the patients’ selection process. Transparency in patients’ selection mitigates biases, thus ensuring study replicability. To enhance the reader’s understanding of the selection process, it is important to avoid redundancy in inclusion and exclusion criteria as in Example#1 and Example#2. Example#2 also further specifies the selection process as consecutive, which is important to declare. Although it largely depends on the writing preferences, it is better to keep the numeric values of eligibility criteria in the results section.
References
- Kocak B, Baessler B, Bakas S, et al (2023) CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII. Insights Imaging 14:75. https://doi.org/10.1186/s13244-023-01415-8
- Cousin F, Louis T, Dheur S, et al (2023) Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors. Cancers 15:1968. https://doi.org/10.3390/cancers15071968
- Ren H, Song H, Wang J, et al (2023) A clinical–radiomics model based on noncontrast computed tomography to predict hemorrhagic transformation after stroke by machine learning: a multicenter study. Insights Imaging 14:52. https://doi.org/10.1186/s13244-023-01399-5