Correct Confidence Research Articles

Digital Otoscopy With Computer-Aided Composite Image Generation: Impact on the Correct Diagnosis, Confidence, and Time.

This study investigated the comparative performance of ear, nose, and throat (ENT) physicians in correctly detecting ear abnormalities when reviewing digital otoscopy imaging using 3 different visualization methods, including computer-assisted composite images called "SelectStitch," single video frame "Still" images, and video clips. The study also explored clinicians' diagnostic confidence levels and the time to make a diagnosis. Clinician diagnostic reader study. Online diagnostic survey of ENT physicians. Nine ENT physicians reviewed digital otoscopy examinations from 86 ears with various diagnoses (normal, perforation, retraction, middle ear effusion, tympanosclerosis). Otoscopy examinations used artificial-intelligence (AI)-based computer-aided composite image generation from a video clip (SelectStitch), manually selected best still frame from a video clip (Still), or the entire video clip. Statistical analyses included comparisons of ability to detect correct diagnosis, confidence levels, and diagnosis times. The ENT physicians' ability to detect ear abnormalities (33.2%-68.7%) varied depending on the pathologies. SelectStitch and Still images were not statistically different in detecting abnormalities (P > .50), but both were different from Video (P < .01). However, the performance improvement observed with Videos came at the cost of significantly longer time to determining the diagnosis. The level of confidence in the diagnosis was positively associated with correct diagnoses, but varied by particular pathology. This study explores the potential of computer-assisted techniques like SelectStitch in enhancing otoscopic diagnoses and time-saving, which could benefit telemedicine settings. Comparable performance between computer-generated and manually selected images suggests the potential of AI algorithms for otoscopy applications.

Human-AI collaboration: Unraveling the effects of user proficiency and AI agent capability in intelligent decision support systems

Artificial intelligence (AI) agents are integral components of modern intelligent decision support systems (IDSS), providing their capability to assist in decision-making processes. Understanding the influence of AI involvement on human responses within these systems is crucial for fostering appropriate reliance on AI advice and ensuring a comfortable user experience. This study delves into the impact of AI agents on decision change, correct rate change, confidence rate change and pleasure rating among users. It explores the main and interactive effects of user proficiency and AI agent capability on user responses. A block-factorial experiment involving 45 participants was conducted. The results indicated that high-capability AI agents were associated with substantial decision change, correct rate change and confidence rate change. Participants with higher proficiency, both subjectively and objectively, exhibited reduced reliance on AI agents for task execution. Notably, the involvement of AI agents led to a slight decline in correct rates for participants of high competence (i.e., participants' independent correct rate exceeds AI agent capability), while still enhancing their confidence levels. Additionally, agents lacking human-like embodiment tended to enhance participants’ pleasure ratings. Individual differences in gender, collaboration experience with AI agent, and stereotype on AI agent capability also shaped the decision changes and pleasure ratings of participants. These findings underscore the presence of complementary competence interaction between user and AI agent within IDSS, and can be applied in designing capability of advisory AI agents with consideration of user proficiency and individual characteristics. Relevance to industryThe findings can be used as guidelines for designing AI agents and specifying their application strategies to integrate the assistance of AI agents effectively in intelligent decision support systems.

On the Estimation of Fit Indices for the Structural Part of a Model

When a researcher proposes an SEM model to explain the dynamics among some latent variables, the real question in model evaluation is the fit of the model’s structural part. A composite index that lumps the fit of the structural part and measurement part does not directly address that question. The need for more attention to structural-level fit has been noted for a long time, but the literature has been limited to conceptual issues such as how to define the structural-level RMSEA or CFI. How to estimate those structural-level fit indices, however, have been overlooked. Traditional estimation methods for composite-fit RMSEA and CFI are not applicable to structural-level fit indices, even if data are complete and normally distributed. In this article, we give correct point estimators and confidence intervals for structural-level RMSEA and CFI. Simulation results suggest that the new methods have satisfactory performance in realistic situations.

Inference for ARMA time series with mildly varying trend

Statistical inference is studied for ARMA time series with a mildly varying smooth trend. Following properly calibrated B-spline estimation of the trend, approximates of the unobserved ARMA series are used instead to estimate ARMA parameters by maximum likelihood. The parameter estimates are shown to be oracally efficient in the sense that they are asymptotically as efficient as if the true trend function was known and removed to obtain the true ARMA series. The mildly varying trend function is estimated by Nadaraya–Watson method with asymptotically correct simultaneous confidence band (SCB). The SCB sprawls over an interval that eventually covers the half real line, instead of a bounded interval in the existing literature, with asymptotic width of the SCB dependent on ARMA coefficients. Simulation experiments corroborate the theoretical findings.

Oracle-efficient estimation and global inferences for variance function of functional data

A new two-step reconstruction-based moment estimator and an asymptotically correct smooth simultaneous confidence band as a global inference tool are proposed for the heteroscedastic variance function of dense functional data. Step one involves spline smoothing for individual trajectory reconstructions and step two employs kernel regression on the individual squared residuals to estimate each trajectory variability. Then by the method of moment an estimator for the variance function of functional data is constructed. The estimation procedure is innovative by synthesizing spline smoothing and kernel regression together, which allows one not only to apply the fast computing speed of spline regression but also to employ the flexible local estimation and the extreme value theory of kernel smoothing. The resulting estimator for the variance function is shown to be oracle-efficient in the sense that it is uniformly as efficient as the ideal estimator when all trajectories were known by “oracle”. As a result, an asymptotically correct simultaneous confidence band for the variance function is established. Simulation results support our asymptotic theory with fast computation. As an illustration, the proposed method is applied to the analyses of two real data sets leading to a number of discoveries.

Behavioral and neural measures of confidence using a novel auditory pitch identification task.

Observers can discriminate between correct versus incorrect perceptual decisions with feelings of confidence. The centro-parietal positivity build-up rate (CPP slope) has been suggested as a likely neural signature of accumulated evidence, which may guide both perceptual performance and confidence. However, CPP slope also covaries with reaction time, which also covaries with confidence in previous studies, and performance and confidence typically covary; thus, CPP slope may index signatures of perceptual performance rather than confidence per se. Moreover, perceptual metacognition-including neural correlates-has largely been studied in vision, with few exceptions. Thus, we lack understanding of domain-general neural signatures of perceptual metacognition outside vision. Here we designed a novel auditory pitch identification task and collected behavior with simultaneous 32-channel EEG in healthy adults. Participants saw two tone labels which varied in tonal distance on each trial (e.g., C vs D, C vs F), then heard a single auditory tone; they identified which label was correct and rated confidence. We found that pitch identification confidence varied with tonal distance, but performance, metacognitive sensitivity (trial-by-trial covariation of confidence with accuracy), and reaction time did not. Interestingly, however, while CPP slope covaried with performance and reaction time, it did not significantly covary with confidence. We interpret these results to mean that CPP slope is likely a signature of first-order perceptual processing and not confidence-specific signals or computations in auditory tasks. Our novel pitch identification task offers a valuable method to examine the neural correlates of auditory and domain-general perceptual confidence.

Oracle-efficient estimation and trend inference in non-stationary time series with trend and heteroscedastic ARMA error

The non-stationary time series often contain an unknown trend and unobserved error terms. The error terms in the proposed model consist of a smooth variance function and the latent stationary ARMA series, which allows heteroscedasticity at different time points. The theoretically justified two-step B-spline estimation method is proposed for the trend and variance function in the model, and then residuals are obtained by removing the trend and variance function estimators from the data. The maximum likelihood estimator (MLE) for the latent ARMA error coefficients based on the residuals is shown to be oracally efficient in the sense that it has the same asymptotic distribution as the infeasible MLE if the trend and variance function were known. In addition to the oracle efficiency, a kernel estimator is obtained for the trend function and shown to converge to the Gumbel distribution. It yields an asymptotically correct simultaneous confidence band (SCB) for the trend function, which can be used to test the specific form of trend. A simulation-based procedure is proposed to implement the SCB, and simulation and real data analysis illustrate the finite sample performance.

Geographic atrophy (GA) secondary to AMD: Online medical education can significantly improve ophthalmologists' knowledge and confidence on evidence‐based guidance for patient management

Aims/Purpose: To determine if online medical education for ophthalmologists (Ophth) could improve knowledge for GA disease impact, patient assessment and confidence in emerging therapies.Methods: Ophth participated in an online 30 min video, 3 expert roundtable discussion with accompanying slides. A repeated pairs pre−/post‐assessment study design was used, assessing three multiple choice knowledge and one confidence question. Statistical significance tests: McNemar's test for individual questions (5% significance level, p &lt; 0.05), paired sample t‐test for overall average correct responses and confidence rating; data collected 16 December 2022 to 22 March 2023. Test completers: 230.Results: At baseline, 3% Ophth answered all three questions correctly, increasing to 37% (p &lt; 0.01) post‐assessment. 58% Ophth reported improved confidence understanding the rationale for targeting the complement system with GA therapies. The activity significantly increased knowledge on impact of GA on time to loss of ability to drive (pre: 7%, post: 52%, p &lt; 0.001), on recall of detrimental lesion characteristics (pre: 40%, post: 67% p &lt; 0.01) as well as capability of automated OCT analysis algorithms to predict GA progression (pre: 51%, post: 69%, p &lt; 0.001). Only 24% Ophth polled currently document &gt;75% patients with advanced atrophic AMD with OCT/FAF imaging.Conclusions: Online medical education in the form of expert roundtable discussions with accompanying slides can significantly improve knowledge on assessing patients with GA using imaging, understand the rationale to treat and increase confidence in understanding the role of complement inhibition. Polling showed a lack in documenting patients for GA progression. Complementary education is warranted during the introduction of a novel therapeutic horizon in GA management in clinical practice to support evidence‐based patient selection guidance for emerging therapies.

Simultaneous inference and uniform test for eigensystems of functional data

The asymptotically correct confidence interval (CI) and simultaneous confidence band (SCB) of any individual eigenvalue and eigenfunction are constructed under dense functional data through B-spline smoothing. Besides, uniform inference procedures for eigensystems with a diverging number of components are novelly developed. The proposed estimators for functional eigensystems employ “oracle” efficiency up to order n, which means they are asymptotically indistinguishable from the estimators conducted by completely observed trajectories, and enjoy computational efficiency with much more convenient spectrum decomposition forms. Furthermore, an extension to two-sample problems is also investigated. Numerical simulation results strongly corroborate the asymptotic theory. Real data analysis for ElectroEncephalogram (EEG) data illustrates applicability of the developed methods.

What evidence-based learning activities help students acquire knowledge, correct confidence in their own knowledge, and accurate self-assessment?

The current study examines the development of higher education students' knowledge, correct confidence in their own knowledge, and accuracy of self-assessment over one semester and if the use of evidence-based learning activities (such as practice testing) is associated with these outcomes. The present study assessed N = 285 university students' use of learning activities, their knowledge, correct confidence, and accuracy of self-assessment five times over the course of a semester in a lecture course. All learning outcomes improved and were explained by learning activity use beyond students' prior knowledge, prior achievement and motivation. Latent profile analyses distinguished five subgroups of students regarding the frequency and combination of learning activity use. Results indicate that students benefitted most from using multiple activities. The study provides evidence that in a realistic learning setting several learning activities contribute to students' knowledge, correct confidence, and accuracy of self-assessment gains. Educational relevance statementThe study identifies positive changes in various learning outcomes, i.e., knowledge, correct confidence, and accuracy, over the course of a semester in a cohort of university students. It also examines students' learning behavior with a person-centered approach and if students learning behavior adds to the explanation of learning outcomes. The results support the benefits of engaging in many different learning activities beyond individual learning prerequisites. The study adds to current research by combining the study of multiple learning activities in a lecture course and the longitudinal investigation of multiple learning outcomes. The study further reinforces the importance of providing evidence-based activities in higher education.

Impact of a teaching strategy to promote evidence-based practice on nursing students’ knowledge and confidence in simulated clinical intervention choices

BackgroundNurses self-efficacy, confidence and their competency for evidence-based practice have a relevant impact in the quality of care provided to patients. However, the implementation of evidence-based practice continues to be limited to date and the relationship between these elements has not been thoroughly understood. Thus, the aim of this study was to analyze the impact on confidence levels of a teaching strategy to promote evidence incorporation into clinical decisions made by student nurses in hypothetical scenarios. Besides, students’ satisfaction with the new teaching strategy was assessed.MethodsThe teaching strategy was asynchronous, on-line and based on multiple-choice questionnaires related to decision making on an intensive care unit patient. Confidence levels were assessed by introducing the scoring tool confidence-based marking. Changes between pre- and post-tests in correct answers, confidence levels and expected-observed ranges of accuracy at each level of certainty were analyzed through non-parametric McNemar’s sign tests for paired-samples differences. To assess students’ satisfaction with the teaching strategy, a mixed-methods approach was followed. Descriptive statistical methods and Qualitative Content Analysis were followed respectively in order to analyze students’ satisfaction.ResultsA total of 165 students completed the assignment, 101 answered the satisfaction survey and 7 participated in the interviews. Statistically significant better scoring and higher confidence levels were found in the post-intervention. Statistically significant differences in expected-observed ranges of accuracy were found for the three levels of certainty. Students were highly satisfied with the proposed task. In the qualitative analysis one category was elaborated which illustrated the students’ perceived added value of this new assignment.ConclusionsOn-line teaching strategies based on clinical scenarios that focus on evidence-based decision-making have the potential to increase the confidence of nursing students. Additionally, interventions designed by teams incorporating clinical nurses, university librarians and academic nurses have the potential to bridge the evidence-practice gap in nursing education.

Practical patient care appraisals with use of new potassium binders in heart failure and chronic kidney diseases.

Hyperkalaemia is a life-threatening condition leading to significant morbidity and mortality. It is common in heart failure and in chronic kidney disease (CKD) patients due to the diseases themselves, which often coexist, the high co-presence of diabetes, the fluctuations in renal function, and the use of some drugs [i.e. renin-angiotensin-aldosterone system (RAAS) inhibitors]. Hyperkalaemia limits their administration or uptitration, thus impacting on mortality. New K+ binders, namely patiromer and sodium zirconium cyclosilicate (ZS-9), are an intriguing option to manage hyperkalaemia in heart failure and/or CKD patients, both to reduce its fatal effects and to let clinicians uptitrate RAAS inhibition. Even if their real impact on strong outcomes is still to be determined, we hereby provide a practical approach to favour their use in routine clinical practice in order to gain the correct confidence and provide an additive tool to heart failure and CKD patients' wellbeing. New trials are welcome to fill the gap in knowledge.

Locally correct confidence intervals for a binomial proportion: A new criteria for an interval estimator

AbstractWell‐recommended methods of forming “confidence intervals” for a binomial proportion give interval estimates that do not actually meet the definition of a confidence interval, in that their coverages are sometimes lower than the nominal confidence level. The methods are favoured because their intervals have a shorter average length than the Clopper–Pearson (gold‐standard) method, whose intervals really are confidence intervals. As the definition of a confidence interval is not being adhered to, another criterion for forming interval estimates for a binomial proportion is needed. In this paper, we suggest a new criterion for forming one‐sided intervals and equal‐tail two‐sided intervals. Methods which meet the criterion are said to yield locally correct confidence intervals. We propose a method that yields such intervals and prove that its intervals have a shorter average length than those of any other method that meets the criterion. Compared with the Clopper–Pearson method, the proposed method gives intervals with an appreciably smaller average length. For confidence levels of practical interest, the mid‐ method also satisfies the new criterion and has its own optimality property. It gives locally correct confidence intervals that are only slightly wider than those of the new method.

A practical revealed preference model for separating preferences and availability effects in marriage formation.

Many demographic problems require models for partnership formation. We consider a model for matchings within a bipartite population where individuals have utility for people based on observed and unobserved characteristics. It represents both the availability of potential partners of different types and the preferences of individuals for such people. We develop an estimator for the preference parameters based on sample survey data on partnerships and population composition. We conduct simulation studies based on the Survey of Income and Program Participation showing that the estimator recovers preference parameters that are invariant under different population availabilities and has the correct confidence coverage.

Impact of Bias Correction of the Least Squares Estimation on Bootstrap Confidence Intervals for Bifurcating Autoregressive Models

The least squares (LS) estimator of the autoregressive coefficient in the bifurcating autoregressive (BAR) model was recently shown to suffer from substantial bias, especially for small to moderate samples. This study investigates the impact of the bias in the LS estimator on the behavior of various types of bootstrap confidence intervals for the autoregressive coefficient and introduces methods for constructing bias-corrected bootstrap confidence intervals. We first describe several bootstrap confidence interval procedures for the autoregressive coefficient of the BAR model and present their bias-corrected versions. The behavior of uncorrected and corrected confidence interval procedures is studied empirically through extensive Monte Carlo simulations and two real cell lineage data applications. The empirical results show that the bias in the LS estimator can have a significant negative impact on the behavior of bootstrap confidence intervals and that bias correction can significantly improve the performance of bootstrap confidence intervals in terms of coverage, width, and symmetry.

Research on the Cultivation of College Students’ National Spirit from the Perspective of the Chinese Dream

Chinese college students are the future of the vigorous development of the motherland, the expectation of the Chinese nation, the successors and builders of the domestic socialist modernization cause, and the main bearers and practitioners of the Chinese dream. The cultivation of college students’ own national cultural spirit is not only related to the overall development of individuals, but also closely related to the future prospects of our country and the survival and development of the entire ethnic minority. Therefore, the cultivation of the national cultural spirit of our college students in the perspective of the Chinese Dream can improve the majority of college students’ sense of identity with the Chinese national culture, adhere to the Marxist ideal, unswervingly take the correct confidence of the socialist line with Chinese characteristics, and actively participate in the grand practice of achieving the Chinese Dream, thus serving as the backbone of the implementation of the Chinese Dream.

Optimizing simulator-based training for emergency transesophageal echocardiography: A randomized controlled trial.

Resuscitative clinician-performed transesophageal echocardiography (TEE) is a relatively novel ultrasound application; however, optimal teaching methods have not been determined. Previous studies have demonstrated that variable practice (VP), where practice conditions are changed, may improve learning of procedural skills compared with blocked practice (BP), where practice conditions are kept constant. We compared VP and BP for teaching resuscitative TEE to emergency medicine residents using a simulator. Emergency medicine residents with no prior TEE experience were randomized to the BP or VP groups. The BP group practiced 10 repetitions of a fixed five-view TEE sequence, while the VP group practiced 10 different random five-view TEE sequences on a simulator. Participants completed a performance assessment immediately after training and a transfer test 2 weeks after training. Ultrasound images and transducer motion metrics were captured by the simulator for blinded analysis. The primary outcome was the percentage of successful views on the transfer test. Twenty-eight participants completed the study (14 in the BP group, 14 in the VP group). The BP group had a higher rate of successful views compared with the VP group on the transfer test (93.6% vs. 77.6%; p= 0.002). The BP group also had higher image quality on a 5-point scale (3.3 vs. 2.9; p= 0.01) and fewer probe angular changes (2982.5 degrees vs. 4239.8 degrees; p= 0.04). There were no statistically significant differences between the groups for the rate of correct diagnoses, confidence level, or scan time. Practicing a fixed sequence of views was more effective than a variable sequence of views for learning resuscitative TEE on a simulator. These results should be validated in TEE scans performed in the clinical environment.

1775. Interns and Residents’ Knowledge and Perspective on Appropriate Use of Antibiotics for Urinary Tract Infections and Skin and Soft Tissue Infections in the Hospital Setting

Abstract Background Antibiotic resistance is recognized as a significant threat to public health worldwide. The rate of inappropriate use of antibiotics in hospitals has been reported as high as 50%. Exposure to suboptimal antibiotic therapy is associated with higher risk of infections with multidrug-resistant organisms and worse clinical outcomes. In academic hospitals, interns and residents are frontline worker who diagnose and treat infectious diseases (ID). The treatment of urinary tract infections (UTIs) and skin and soft tissue infections (SSTIs) is a common clinical scenario managed by these trainees. Methods A structured, anonymous questionnaire was sent via institutional email to internal medicine interns and residents rotating in three academic hospitals in Miami, FL. The 23-item questionnaire included knowledge questions on rational use of antibiotics and duration of therapy for complicated and uncomplicated UTIs, SSTIs, correct renal dosing of frequently used antibiotics and antivirals, and perception of confidence in antibiotic prescription. Data were entered to Microsoft Excel spreadsheet, then analyzed using JMP Pro software. Results A total of 36 preliminary and categorical interns, and 38 second-and third-year residents completed the initial questionnaire. The mean correct knowledge score on the treatment of UTIs was 52.8% (19/36) for interns and 76.3% (29/38) for residents, respectively. For SSTIs, the scores were 38.9% (14/36) and 65.8% (25/38); and for correct renal dosing of antibiotics, the scores were 47.2% (17/36) vs. 73.7% (28/38). Most of the interns (58.3%, 21/36) did not feel confident prescribing antibiotics for these conditions compared with 18.4% (7/38) of the residents. Conclusion In this cross-sectional study, interns had lower correct knowledge scores and confidence when prescribing antibiotics for UTIs and SSTIs compared to residents. There is room for improvement in advanced years of training. Further studies are necessary to assess the impact of implementing educational strategies to promote the appropriate use of antibiotics. We plan to address this through a pre-/post implementation We will compare antibiotic prescribing patterns and patient outcomes for these indications at one of our three clinical training sites. Disclosures All Authors: No reported disclosures.

Optimal confidence intervals for the relative risk andodds ratio.

The relative risk and odds ratio are widely used in many fields, including biomedical research, to compare two treatments. Extensive research has been done to infer the two parameters through approximate or exact confidence intervals. However, these intervals may be liberal or conservative. A natural question is whether the intervals can be further improved in maintaining the correct confidence coefficient of an approximate interval or shortening an exact but conservative interval. In this article, when two independent binomials are observed we offer an effort to improve any of the existing intervals by applying the -function method. In particular, if the given interval is approximate, then the improved interval is exact; if the given interval is exact, then the improved interval is a subset of the given interval. This method is also applied multiple times to the improved intervals until the final resultant interval cannot be shortened any further. To demonstrate the effectiveness of the method, we use three real datasets to illustrate in detail how several good intervals in practice are improved. Two exact intervals are then recommended for estimating each of the two parameters in different scenarios.

An empirical method for mitigating an excess up-scattering mass bias on the weak lensing mass estimates for shear-selected cluster samples

Abstract An excess up-scattering mass bias on a weak lensing cluster mass estimate is a statistical bias that an observed weak lensing mass (Mobs) of a cluster of galaxies is, in a statistical sense, larger than its true mass (Mtrue) because of a higher chance of up-scattering than that of down-scattering due to random noises in a weak lensing cluster shear profile. This non-symmetric scattering probability is caused by a monotonically decreasing cluster mass function with increasing mass. We examine this bias (defined by b = Mobs/Mtrue) in weak lensing shear-selected clusters, and present an empirical method for mitigating it. In so doing, we perform the standard weak lensing mass estimate of realistic mock clusters, and find that the weak lensing mass estimate based on the standard χ2 analysis gives a statistically correct confidence intervals, but resulting best-fitting masses are biased high on average. Our correction method uses the framework of the standard Bayesian statistics with the prior of the probability distribution of the cluster mass and concentration parameter from recent empirical models. We test our correction method using mock weak lensing clusters, and find that the method works well with resulting corrected Mobs-bin averaged mass biases being close to unity within ${\sim}10\%$. We applied the correction method to weak lensing shear-selected cluster sample of Hamana, Shirasaki, and Lin (2020, PASJ, 72, 78), and present bias-corrected weak lensing cluster masses.