Non-Selective Thoracic Computed Tomography in Trauma Patients Results in Injury Severity Score Inflation

Introduction Extensive research relying on Injury Severity Scores (ISS) reports a mortality benefit from routine non-selective thoracic CTs (an integral part of pan-computed tomography (pan-CT)s). Recent research suggests this mortality benefit may be artifact. We hypothesized that the use of pan-CTs inflates ISS categorization in patients, artificially affecting admission rates and apparent mortality benefit. Methods Eight hundred and eleven patients were identified with an ISS >15 with significant findings in the chest area. Patient charts were reviewed and scores were adjusted to exclude only occult injuries that did not affect treatment plan. Pearson chi-square tests and multivariable logistic regression were used to compare adjusted cases vs non-adjusted cases. Results After adjusting for inflation, 388 (47.8%) patients remained in the same ISS category, 378 (46.6%) were reclassified into 1 lower ISS category, and 45 (5.6%) patients were reclassified into 2 lower ISS categories. Patients reclassified by 1 category had a lower rate of mortality ( P < 0.001), lower median total hospital LOS ( P < .001), ICU days ( P < .001), and ventilator days ( P = 0.008), compared to those that remained in the same ISS category. Conclusion Injury Severity Score inflation artificially increases survival rate, perpetuating the increased use of pan-CTs. This artifact has been propagated by outdated mortality prediction calculation methods. Thus, prospective evaluations of algorithms for more selective CT scanning are warranted.

The Fakeability of Personality Measurement with Graded Paired Comparisons

This pre-registered study compares the faking resistance of Likert scales and graded paired comparisons (GPCs) analyzed with Thurstonian IRT models. Based on findings on other forced-choice formats, we hypothesized that GPCs would be more resistant to faking than Likert scales by resulting in lower score inflation and better recovery of applicants’ true (i.e., honest) trait scores. A total of N = 573 participants completed either the Likert or GPC version of a personality questionnaire first honestly and then in an applicant scenario. Results show that participants were able to increase their scores in both the Likert and GPC format, though their score inflation was smaller in the GPC than the Likert format. However, GPCs did not exhibit higher honest–faking correlations than Likert scales; under certain conditions, we even observed negative associations. These results challenge mean score inflation as the dominant paradigm for judging the utility of foeced-choice questionnaires in high-stakes situations. Even if FC factor scores are less inflated, their ability to recover true trait standings in high-stakes situations might be lower compared with Likert scales. Moreover, in the GPC format, faking effects correlated almost perfectly with the social desirability differences of the corresponding statements, highlighting the importance of matching statements equal in social desirability when constructing forced-choice questionnaires.

Macroeconomic forecasting in Poland: The role of forecasting competitions

Macroeconomic forecasters are often believed to idealistically work on improving the accuracy of their estimates based on for example the Root Mean Squared Error (RMSE). Unfortunately, reality is far more complex. Forecasters are not awarded equally for each of their estimates. They have their targets of acquiring publicity or to earn prestige. This article aims to study the results of Parkiet's competitions of macroeconomic forecasting during 2015–2019. Based on a logit model, we analyse whether more accurate forecasting of some selected macroeconomic variables (e.g. inflation) increases the chances of winning the competition by a greater degree comparing to the others. Our research shows that among macroeconomic variables three groups have a significant impact on the final score: inflation (CPI and core inflation), the labour market (employment in the enterprise sector and unemployment rate) and financial market indicators (EUR/PLN and 10-year government bond yields). Each group is characterised by a low disagreement between forecasters. In the case of inflation, we found evidence that some forecasters put a greater effort to score the top place. There is no evidence that forecasters are trying to somehow exploit the contest.

HirePeer: Impartial Peer-Assessed Hiring at Scale in Expert Crowdsourcing Markets

Expert crowdsourcing (e.g., Upwork.com) provides promising benefits such as productivity improvements for employers, and flexible working arrangements for workers. Yet to realize these benefits, a key persistent challenge is effective hiring at scale. Current approaches, such as reputation systems and standardized competency tests, develop weaknesses such as score inflation over time, thus degrading market quality. This paper presents HirePeer, a novel alternative approach to hiring at scale that leverages peer assessment to elicit honest assessments of fellow workers' job application materials, which it then aggregates using an impartial ranking algorithm. This paper reports on three studies that investigate both the costs and the benefits to workers and employers of impartial peer-assessed hiring. We find, to solicit honest assessments, algorithms must be communicated in terms of their impartial effects. Second, in practice, peer assessment is highly accurate, and impartial rank aggregation algorithms incur a small accuracy cost for their impartiality guarantee. Third, workers report finding peer-assessed hiring useful for receiving targeted feedback on their job materials.

Clinical Teaching Improves with Resident Evaluation and Feedback

Background The literature is mixed on whether evaluation and feedback to clinical teachers improves clinical teaching. This study sought to determine whether resident-provided numerical evaluation and written feedback to clinical teachers improved clinical teaching scores. Methods Anesthesia residents anonymously provided numerical scores and narrative comments to faculty members who provided clinical teaching. Residents returned 19,306 evaluations between December 2000 and May 2006. Faculty members received a quantitative summary report and all narrative comments every 6 months. Residents also filled out annual residency program evaluations in which they listed the best and worst teachers in the department. Results The average teaching score for the entire faculty rose over time and reached a plateau with a time constant of approximately 1 yr. At first, individual faculty members had average teaching scores that were numerically diverse. Over time, the average scores became more homogeneous. Faculty members ranked highest by teaching scores were also most frequently named as the best teachers. Faculty members ranked lowest by teaching scores were most frequently named as the worst teachers. Analysis of ranks, differential improvement in scores, and a decrease in score diversity effectively ruled out simple score inflation as the cause for increased scores. An increase in teaching scores was most likely due to improved teaching. Conclusions A combination of evaluation and feedback, including comments on areas for improvement, was related to a substantial improvement in teaching scores. Clinical teachers are able to improve by using feedback from residents.