Projects per year
Objective: To establish the prevalence of red dot markers in a sample of wrist radiographs and to identify any anatomical and/or pathological characteristics that predict “incorrect” red dot classification. Methods: Accident and emergency (A&E) wrist cases from a digital imaging and communications in medicine/ digital teaching library were examined for red dot prevalence and for the presence of several anatomical and pathological features. Binary logistic regression analyses were run to establish if any of these features were predictors of incorrect red dot classification. Results: 398 cases were analysed. Red dot was “incorrectly” classified in 8.5% of cases; 6.3% were “false negatives” (“FNs”) and 2.3% false positives (FPs) (one decimal place). Old fractures [odds ratio (OR), 5.070 (1.256–20.471)] and reported degenerative change [OR, 9.870 (2.300–42.359)] were found to predict FPs. Frykman V [OR, 9.500 (1.954–46.179)], Frykman VI [OR, 6.333 (1.205–33.283)] and non-Frykman positive abnormalities [OR, 4.597 (1.264–16.711)] predict “FNs”. Old fractures and Frykman VI were predictive of error at 90% confidence interval (CI); the rest at 95% CI. Conclusion: The five predictors of incorrect red dot classification may inform the image interpretation training of radiographers and other professionals to reduce diagnostic error. Verification with larger samples would reinforce these findings. Advances in knowledge: All healthcare providers strive to eradicate diagnostic error. By examining specific anatomical and pathological predictors on radiographs for such error, as well as extrinsic factors that may affect reporting accuracy, image interpretation training can focus on these “problem” areas and influence which radiographic abnormality detection schemes are appropriate to implement in A&E departments.