Midsession reversal learning by pigeons: Effect on accuracy of increasing the number of stimuli associated with one of the alternatives.

The midsession reversal task involves a simultaneous discrimination in which choice of one stimulus (S1) is correct for the first 40 trials and choice of the other stimulus (S2) is correct for the last 40 trials of each 80-trial session. When pigeons are trained on the midsession reversal task, they appear to use the passage of time from the start of the session as a cue to reverse. As the reversal approaches, they begin to make anticipatory errors, choosing S2 early, and following the reversal they make perseverative errors, continuing to choose S1. Recent research suggests that anticipatory errors can be reduced (while not increasing perseverative errors) by reducing the probability of reinforcement for correct S2 choices from 100% to 20%. A similar effect can be found by increasing the response requirement for choice of S2 from one peck to ten pecks. In the present experiments, we asked if a similar effect could be attained by increasing the number of stimuli that, over trials, could serve as S2. Instead, in both experiments, we found that increasing the number of S2 stimuli actually increased the number of anticipatory errors. Several interpretations of this result are provided, including the possibility that attention to the variable S2 stimuli may have interfered with attention to the S1 stimulus.

Overcoming barriers to lung cancer screening using a systemwide approach with additional focus on the non-screened.

BACKGROUND: The lung cancer screening program at St Elizabeth Healthcare (Kentucky, USA) began in 2013. Over 33,000 low-dose computed tomography lung cancer screens have been performed. From 2015 through 2021, 2595 lung cancers were diagnosed systemwide. A Screening Program with Impactful Results from Early Detection, reviews that experience; 342 (13.2%) were diagnosed by screening and 2253 (86.8%) were non-screened. As a secondary objective, the non-screened cohort was queried to determine how many additional individuals could have been screened, identifying barriers and failures to meet eligibility. METHODS: Our QlikSense database extracted the lung cancer patients from the Cancer Patient Data and Management System, and identified and categorized them separately as screened or non-screened populations. Stage distribution was compared in screened and non-screened groups. Those meeting age criteria, with any smoking history, were further queried for screening eligibility, accessing the electronic medical record smoking history and audit trail, and determining if enough information was available to substantiate screening eligibility. The same methodology was applied to CMS 2015 and USPSTF 2021 criteria. RESULTS: The screened and non-screened patients were accounted for in a stage migration chart demonstrating clear shift to early stage among screened lung cancer patients. Additionally, analysis of non-screened individuals is presented. CONCLUSION: Of the St Elizabeth Healthcare eligible patients attributed to primary care providers, 49.6% were screened in 2021. Despite this level of success, this study highlighted a sizeable pool of additional individuals that could have been screened. We are shifting focus to the non-screened pool of patients that meet eligibility, further enhancing the impact on our community.