Autonomous Airliners Anytime Soon?

This research seeks to extend the body of knowledge on factors influential in the decision to fly on an autonomous airliner as a passenger. Only a handful of studies have probed this direct question in the last 16 years, but the data is showing a growing public acceptance of this type of travel. Pivotal in this consideration is the basic element of trust – trust in automated airliners and trust in the airline and Air Traffic Control systems which are responsible for autonomous airliners. Human trust has many forms and manifestations, but in the end, it is a dichotomous or binary choice; either a human does or does not trust. Longitudinally comparing the previous autonomous airliner research samples was technically impure because the respondent pools were dissimilar in age demographics, vocational backgrounds, and nationality. Nevertheless, a current, United States-focused sampling was taken to compare with the 16-year historical data available and explore trends in this emerging discussion

Predicting fitness to practise events in international medical graduates who registered as UK doctors via the Professional and Linguistic Assessments Board (PLAB) system: a national cohort study

Background International medical graduates working in the UK are more likely to be censured in relation to fitness to practise compared to home graduates. Performance on the General Medical Council’s (GMC’s) Professional and Linguistic Assessments Board (PLAB) tests and English fluency have previously been shown to predict later educational performance in this group of doctors. It is unknown whether the PLAB system is also a valid predictor of unprofessional behaviour and malpractice. The findings would have implications for regulatory policy. Methods This was an observational study linking data relating to fitness to practise events (referral or censure), PLAB performance, demographic variables and English language competence, as evaluated via the International English Language Test System (IELTS). Data from 27,330 international medical graduates registered with the GMC were analysed, including 210 doctors who had been sanctioned in relation to at least one fitness to practise issue. The main outcome was risk of eventual censure (including a warning). Results The significant univariable educational predictors of eventual censure (versus no censures or referrals) were lower PLAB part 1 (hazard ratio [HR], 0.99; 95% confidence interval, 0.98 to 1.00) and part 2 scores (HR, 0.94; 0.91 to 0.97) at first sitting, multiple attempts at both parts of the PLAB, lower IELTS reading (HR, 0.79; 0.65 to 0.94) and listening scores (HR, 0.76; 0.62 to 0.93) and higher IELTS speaking scores (HR, 1.28; 1.04 to 1.57). Multiple resits at either part of the PLAB and higher IELTS speaking score (HR, 1.49; 1.20 to 1.84) were also independent predictors of censure. We estimated that the proposed limit of four attempts at both parts of the PLAB would reduce the risk in this entire group by only approximately two censures per 5 years in this group of doctors. Conclusions Making the PLAB, or any replacement assessment, more stringent and raising the required standards of English reading and listening may result in fewer fitness to practice events in international medical graduates. However, the number of PLAB resits permitted would have to be further capped to meaningfully impact the risk of sanctions in this group of doctor

Pervasive gene content variation and copy number variation in maize and its undomesticated progenitor

Individuals of the same species are generally thought to have very similar genomes. However, there is growing evidence that structural variation in the form of copy number variation (CNV) and presence-absence variation (PAV) can lead to variation in the genome content of individuals within a species. Array comparative genomic hybridization (CGH) was used to compare gene content and copy number variation among 19 diverse maize inbreds and 14 genotypes of the wild ancestor of maize, teosinte. We identified 479 genes exhibiting higher copy number in some genotypes (UpCNV) and 3410 genes that have either fewer copies or are missing in the genome of at least one genotype relative to B73 (DownCNV/PAV). Many of these DownCNV/PAV are examples of genes present in B73, but missing from other genotypes. Over 70% of the CNV/PAV examples are identified in multiple genotypes, and the majority of events are observed in both maize and teosinte, suggesting that these variants predate domestication and that there is not strong selection acting against them. Many of the genes affected by CNV/PAV are either maize specific (thus possible annotation artifacts) or members of large gene families, suggesting that the gene loss can be tolerated through buffering by redundant functions encoded elsewhere in the genome. While this structural variation may not result in major qualitative variation due to genetic buffering, it may significantly contribute to quantitative variation

Predicting need for hospital admission in patients with traumatic brain injury or skull fractures identified on CT imaging : a machine learning approach

Background: Patients with mild traumatic brain injury on CT scan are routinely admitted for inpatient observation. Only a small proportion of patients require clinical intervention. We recently developed a decision rule using traditional statistical techniques that found neurologically intact patients with isolated simple skull fractures or single bleeds <5 mm with no preinjury antiplatelet or anticoagulant use may be safely discharged from the emergency department. The decision rule achieved a sensitivity of 99.5% (95% CI 98.1% to 99.9%) and specificity of 7.4% (95% CI 6.0% to 9.1%) to clinical deterioration. We aimed to transparently report a machine learning approach to assess if predictive accuracy could be improved. Methods: We used data from the same retrospective cohort of 1699 initial Glasgow Coma Scale (GCS) 13–15 patients with injuries identified by CT who presented to three English Major Trauma Centres between 2010 and 2017 as in our original study. We assessed the ability of machine learning to predict the same composite outcome measure of deterioration (indicating need for hospital admission). Predictive models were built using gradient boosted decision trees which consisted of an ensemble of decision trees to optimise model performance. Results: The final algorithm reported a mean positive predictive value of 29%, mean negative predictive value of 94%, mean area under the curve (C-statistic) of 0.75, mean sensitivity of 99% and mean specificity of 7%. As with logistic regression, GCS, severity and number of brain injuries were found to be important predictors of deterioration. Conclusion: We found no clear advantages over the traditional prediction methods, although the models were, effectively, developed using a smaller data set, due to the need to divide it into training, calibration and validation sets. Future research should focus on developing models that provide clear advantages over existing classical techniques in predicting outcomes in this population

Predictive validity of the UK clinical aptitude test in the final years of medical school:a prospective cohort study

Peer reviewedPublisher PD

The UK medical education database (UKMED) what is it?:Why and how might you use it?

The development of UKMED has not been directly funded by but has received support from the General Medical Council and Medical Schools Council as well as staff time from all participating organisations.Peer reviewedPublisher PD