Single-visit surveys of plots are often used for estimating the abundance of species of conservation concern. Less-than-perfect availability and detection of individuals can bias estimates if not properly accounted for. We developed field methods and a Bayesian model that accounts for availability and detection bias during single-visit visual plot surveys. We used simulated data to test the accuracy of the method under a realistic range of generating parameters and applied the method to Florida\'s east coast diamondback terrapin in the Indian River Lagoon system, where they were formerly common but have declined in recent decades. Simulations demonstrated that the method produces unbiased abundance estimates under a wide range of conditions that can be expected to occur in such surveys. Using terrapins as an example we show how to include covariates and random effects to improve estimates and learn about species-habitat relationships. Our method requires only counting individuals during short replicate surveys rather than keeping track of individual identity and is simple to implement in a variety of point count settings when individuals may be temporarily unavailable for observation. We provide examples in R and JAGS for implementing the model and to simulate and evaluate data to validate the application of the method under other study conditions.

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Cognitive biases, such as the availability heuristic or availability bias, can inadvertently affect patient outcomes. These biases may be magnified during times of heightened awareness of a particular disease. Failure to identify cognitive biases when managing patients during the coronavirus disease 2019 (COVID-19) pandemic can delay the institution of the right treatment option and result in poor health outcomes. We present a case of delayed diagnosis of Legionella pneumonia due to COVID-19-related availability bias. We discuss some methods to mitigate the effects of this bias and the importance of challenging trainees to recognize these pitfalls in medical training.

This chapter provides a broad overview of the use of individual participant (sometimes referred to as patient) data (IPD ) within meta-analyses, the associated advantages of using IPD in meta-analysis compared to aggregate data, and when IPD should be used in meta-analysis.This chapter also outlines the steps of conducting an IPD meta-analysis, with practical guidance relating to requesting and obtaining IPD for meta-analysis. Challenges that can be associated with conducting an IPD meta-analysis are also discussed, including consideration of availability bias, when a subset of the relevant IPD is not available for meta-analysis.

Invasive mucinous adenocarcinoma is a multi-centric adenocarcinoma that accounts for less than 5% of all lung cancer diagnoses. The most common presenting symptoms (cough, sputum production, and chest pain) in conjunction with its radiographic findings (patchy, multi-lobar infiltrates) make invasive mucinous adenocarcinoma challenging to distinguish from both infectious and inflammatory pneumonia. However, due to its aggressive nature, invasive mucinous adenocarcinoma should be considered if a presumed case of pneumonia lacks symptoms of infection (e.g., fever, leukocytosis) and/or does not respond to antibiotics. We report the case of a 75-year-old male who was admitted in the setting of a presumed case of recurrent pneumonia, which had failed to respond to prior antibiotic therapy. Further workup, including trans-bronchial biopsy, confirmed mucinous adenocarcinoma with a lepidic pattern. This case highlights the importance of establishing a broad differential in the setting of unresolved pneumonia following appropriate antibiotic coverage.

We anticipate that unmanned aerial vehicles will become popular wildlife survey platforms. Because detecting animals from the air is imperfect, we develop a mark-recapture line transect method using two digital cameras, possibly mounted on one aircraft, which cover the same area with a short time delay between them. Animal movement between the passage of the cameras introduces uncertainty in individual identity, so individual capture histories are unobservable and are treated as latent variables. We obtain the likelihood for mark-recapture line transects without capture histories by automatically enumerating all possibilities within segments of the transect that contain ambiguous identities, instead of attempting to decide identities in a prior step. We call this method \"Latent Capture-history Enumeration\" (LCE). We include an availability model for species that are periodically unavailable for detection, such as cetaceans that are undetectable while diving. External data are needed to estimate the availability cycle length, but not the mean availability rate, if the full availability model is employed. We compare the LCE method with the recently developed cluster capture-recapture method (CCR), which uses a Palm likelihood approximation, providing the first comparison of CCR with maximum likelihood. The LCE estimator has slightly lower variance, more so as sample size increases, and close to nominal coverage probabilities. Both methods are approximately unbiased. We illustrate with semisynthetic data from a harbor porpoise survey.

Bias in reasoning rather than knowledge gaps has been identified as the origin of most diagnostic errors. However, the role of knowledge in counteracting bias is unclear.
To examine whether knowledge of discriminating features (findings that discriminate between look-alike diseases) predicts susceptibility to bias.
Three-phase randomized experiment. Phase 1 (bias-inducing): Participants were exposed to a set of clinical cases (either hepatitis-IBD or AMI-encephalopathy). Phase 2 (diagnosis): All participants diagnosed the same cases; 4 resembled hepatitis-IBD, 4 AMI-encephalopathy (but all with different diagnoses). Availability bias was expected in the 4 cases similar to those encountered in phase 1. Phase 3 (knowledge evaluation): For each disease, participants decided (max. 2 s) which of 24 findings was associated with the disease. Accuracy of decisions on discriminating features, taken as a measure of knowledge, was expected to predict susceptibility to bias.
Internal medicine residents at Erasmus MC, Netherlands.
The frequency with which higher-knowledge and lower-knowledge physicians gave biased diagnoses based on phase 1 exposure (range 0-4). Time to diagnose was also measured.
Sixty-two physicians participated. Higher-knowledge physicians yielded to availability bias less often than lower-knowledge physicians (0.35 vs 0.97; p = 0.001; difference, 0.62 [95% CI, 0.28-0.95]). Whereas lower-knowledge physicians tended to make more of these errors on subjected-to-bias than on not-subjected-to-bias cases (p = 0.06; difference, 0.35 [CI, - 0.02-0.73]), higher-knowledge physicians resisted the bias (p = 0.28). Both groups spent more time to diagnose subjected-to-bias than not-subjected-to-bias cases (p = 0.04), without differences between groups.
Knowledge of features that discriminate between look-alike diseases reduced susceptibility to bias in a simulated setting. Reflecting further may be required to overcome bias, but succeeding depends on having the appropriate knowledge. Future research should examine whether the findings apply to real practice and to more experienced physicians.

Objective Empirical evidence on the availability bias associated with diagnostic errors is still insufficient. We investigated whether or not recent experience with clinical problems can lead physicians to make diagnostic errors due to availability bias and whether or not reflection counteracts this bias. Methods Forty-six internal medicine residents were randomly divided into a control group (CG) and experimental group (EG). Among the eight clinical cases used in this study, three experimental cases were similar to the disease of dengue fever (DF) but exhibited different diagnoses, one was actually DF, and the other four filler cases were not associated with DF. First, only the EG received information on DF, while the CG knew nothing about this study. Then, six hours later, all participants were asked to diagnose eight clinical cases via nonanalytic reasoning. Finally, four cases were diagnosed again via reflective reasoning. Results In stage 2, the average score of the CG in the diagnosis of experimental cases was significantly higher than that of the filler cases (0.80 vs. 0.59, p<0.01), but the EG\'s average score in the two types of cases was not significantly different (0.66 vs. 0.64, p=0.756). The EG and CG had significantly different scores for each experimental case, while no difference was observed in the filler cases. The proportion of diseases incorrectly diagnosed as DF among experimental cases ranged from 71% to 100% in the EG. There were no significant differences between the mean diagnostic accuracy scores obtained by nonanalytic reasoning and those obtained by the reflective reasoning in any cases. Conclusion Availability bias led to diagnostic errors. Misdiagnoses cannot always be repaired solely by adopting a reflective approach.

Objectives Diagnostic reasoning has been shown to be influenced by a prior similar patient case. However, it is unclear whether this process influences diagnostic error rates or whether clinicians at all experience levels are equally susceptible. The present study measured the influence of specific prior exposure and experience level on diagnostic accuracy. Methods To create the experience of prior exposure, participants (pre-clerkship medical students, emergency medicine residents, and faculty) first verified diagnoses of clinical vignettes. The influence of prior exposures was measured using equiprobable clinical vignettes; indicating two diagnoses. Participants diagnosed equiprobable cases that were: 1) matched to exposure cases (in one of three conditions: a) similar patient features, similar clinical features; b) dissimilar patient features, similar clinical features; c) similar patient features, dissimilar clinical features), or 2) not matched to any prior case (d) no exposure). A diagnosis consistent with a matched exposure case was scored correct. Cases with no prior exposure had no matched cases, hence validated the equiprobable design. Results Diagnosis A represented 47% of responses in condition d, but there was no influence of specific similarity of patient characteristics for Diagnosis A, F(3,712)=7.28, p=0.28 or Diagnosis B, F(3,712)=4.87, p=0.19. When re-scored based on matching both equiprobable diagnoses, accuracy was high, but favored faculty (n=40) 98%, and residents (n=39) 98% over medical students (n=32) 85%, F(2,712)=35.6, p<0.0001. Accuracy for medical students was 84, 87, 94, and 73% for conditions a-d, respectively, interaction F(2,712)=3.55, p<0.002. Conclusions The differential diagnosis of pre-clerkship medical students improved with prior exposure, but this was unrelated to specific case or patient features. The accuracy of medical residents and staff was not influenced by prior exposure.

Results of meta-analyses are potentially valuable for informing environmental policy and practice decisions. However, selective sampling of primary studies through searches exclusively using widely used bibliographic platform(s) could bias estimates of effect sizes. Such search strategies are common in environmental evidence reviews, and if risk of bias can be detected, this would provide the first empirical evidence that comprehensiveness of searches needs to be improved. We compare the impact of using single and multiple bibliographic platform(s) searches vs more comprehensive searches on estimates of mean effect sizes. We used 137 published meta-analyses, based on multiple source searches, analyzing 9388 studies: 8095 sourced from commercially published articles; and 1293 from grey literature and unpublished data. Single-platform and multiple-platform searches missed studies in 100 and 80 of the meta-analyses, respectively: 52 and 46 meta-analyses provided larger-effect estimates; 32 and 28 meta-analyses provided smaller-effect estimates; eight and four meta-analyses provided opposite direction of estimates; and two each were unable to estimate effects due to missing all studies. Further, we found significant positive log-linear relationships between proportions of studies missed and the deviations of mean effect sizes, suggesting that as the number of studies missed increases, deviation of mean effect size is likely to expand. We also found significant differences in mean effect sizes between indexed and non-indexed studies for 35% of meta-analyses, indicating high risk of bias when the searches were restricted. We conclude that the restricted searches are likely to lead to unrepresentative samples of studies and biased estimates of true effects.