Mlady, Gary

Abstract Postmortem computed tomography (PMCT) has been integrated into the practice of many forensic pathologists. To evaluate the utility of PMCT in supplementing and/or supplanting medicolegal autopsy, we conducted a prospective double‐blind comparison of abnormal findings reported by the autopsy pathologist with those reported by a radiologist reviewing the PMCT. We reviewed 890 cases: 167 with blunt force injury (BFI), 63 with pediatric trauma (under 5 years), 203 firearm injuries, and 457 drug poisoning deaths. Autopsy and radiology reports were coded using the Abbreviated Injury Scale and abnormal findings and cause of death (COD) were compared for congruence in consensus conferences with novel pathologists and radiologists. Overall sensitivity for recognizing abnormal findings was 71% for PMCT and 74.6% for autopsy. Sensitivities for PMCT/autopsy were 74%/73.1% for BFI, 61.5%/71.4% for pediatric trauma, 84.9%/83.7% for firearm injuries, and 56.5%/66.4% for drug poisoning deaths. COD assigned by reviewing PMCT/autopsy was correct in 88%/95.8% of BFI cases, 99%/99.5% of firearm fatalities, 82.5%/98.5% of pediatric trauma deaths, and 84%/100% of drug poisoning deaths of individuals younger than 50. Both autopsy and PMCT were imperfect in recognizing injuries. However, both methods identified the most important findings and are sufficient to establish COD in cases of BFI, pediatric trauma, firearm injuries and drug poisoning in individuals younger than 50. Ideally, all forensic pathologists would have access to a CT scanner and a consulting radiologist. This would allow a flexible approach that meets the diagnostic needs of each case and best serves decedents' families and other stakeholders.

Abstract Objectives Implicit bias is common and is thought to drive discriminatory behaviour. Having previously demonstrated discrimination against specific applicant demographics by academic radiology departments in a simulated resident selection process, the authors sought to better understand the relationship between implicit bias and discrimination, as well as the potential and mechanisms for their mitigation. Methods A total of 51 faculty reviewers at three academic radiology departments, who had participated in a 2017 audit study in which they were shown to treat applicants differently based on race or ethnicity and physical appearance, were invited to complete testing for implicit racial and weight bias using the Implicit Association Test in 2019. Respondents were also surveyed regarding awareness of their own personal racial and weight biases, as well as any prior participation in formal diversity training. Comparisons were made between implicit bias scores and applicant ratings, as well as between diversity training and self‐awareness of bias. Results A total of 31 out of 51 faculty reviewers (61%) completed and submitted results of race and weight Implicit Association Tests. A total of 74% (23/31) reported implicit anti‐obese bias, concordant with discrimination demonstrated in the resident selection simulation, in which obese applicants were rated 0.40 standard deviations (SDs) lower than non‐obese applicants ( P < .001). A total of 71% (22/31) reported implicit anti‐Black bias, discordant with application ratings, which were 0.47 SDs higher for Black than for White applicants ( P < .001). A total of 84% (26/31) of participants reported feeling self‐aware of potential racial bias at the time of application review, significantly higher than the 23% (7/31) reporting self‐awareness of potential anti‐obese bias ( P < .001). Participation in formal diversity training was not associated with implicit anti‐Black or anti‐fat bias, nor with self‐reported awareness of potential racial or weight‐based bias (all P > .2). Conclusions These findings suggest that implicit bias, as measured by the Implicit Association Test, does not inevitably lead to discrimination, and that personal awareness of implicit biases may allow their mitigation.

Purpose To evaluate for appearance-based discrimination in the selection of radiology residents. Method A deception study simulating the resident selection process examined the impact of attractiveness and obesity on resident selection. Seventy-four core faculty from 5 academic radiology departments reviewed mock residency applications in September and October 2017. Each application included demographic information and a photograph, representing a prespecified distribution of facial attractiveness and obesity, combined with randomized academic and supporting variables. Reviewers independently scored applications for interview desirability. Reviewer scores and application variables were compared using linear mixed fixed- and random-effects models. Results Reviewers evaluated 5,447 applications (mean: 74 applications per reviewer). United States Medical Licensing Examination Step 1 scores were the strongest predictor of reviewer rating (B = 0.35 [standard error (SE) = 0.029]). Applicant facial attractiveness strongly predicted rating (attractive vs unattractive, B = 0.30 [SE = 0.056]; neutral vs unattractive, B = 0.13 [SE = 0.028]). Less influential but still significant predictors included race/ethnicity (B = 0.25 [SE = 0.059]), preclinical class rank (B = 0.25 [SE = 0.040]), clinical clerkship grades (B = 0.23 [SE = 0.034]), Alpha Omega Alpha membership (B = 0.21 [SE = 0.032]), and obesity (vs not obese) (B = −0.14 [SE = 0.024]). Conclusions Findings provide preliminary evidence of discrimination against facially unattractive and obese applicants in radiology resident selection. Obesity and attractiveness were as influential in applicant selection for interview as traditional medical school performance metrics. Selection committees should invoke strategies to detect and manage appearance-based bias.