Background: The adult bone marrow transplant (BMT) unit at an urban academic medical center in the Midwest reported the highest number of central line-associated bloodstream infection cases across the health system in 2022 and 2023 and notably had the second-highest volume of blood culture specimens collected when compared with other patient care units. Statistical analysis comparing BMT patients to a sample group of oncology patients with the same length of stay and central line days demonstrated that BMT patients had a median of 17 blood cultures per admission compared to 7 in the sample group (p-value 0.000). Moreover, a review of 21 weeks of BMT patient blood culture specimen results suggested that patients were undergoing cultures who were unlikely to have bacteremia or sepsis. Method: An interdisciplinary team created a nurse-driven, clinical decision-making algorithm to refine the release of blood cultures from a conditional order set for BMT patients. The objective of the algorithm was to safely reduce the number of blood culture specimens. It includes an updated fever threshold to align with national neutropenic fever guidelines, consideration for new-onset clinical instability, source of specimen collection, and time from the most recent blood culture. Analysis was completed on 827 cultures over 102 patient admissions in the pre-intervention period and 527 cultures over 162 patient admissions in the post-period. Balancing measures based on escalation of care were assessed by chart review. Results: When comparing blood culture specimens among BMT patients, the median specimen count per admission in the pre-intervention period was 6.0 (IQR = 3.5, 10.0), compared to 2.5 (IQR = 0.0, 5.0) specimens in the post-intervention period (p-value = 0.000). 37.7% of patient admissions were not cultured in the post-intervention period whereby 100% of patient admissions were cultured in the pre-intervention period. Of the 48 rapid responses, 10 intensive care unit transfers, and 1 code blue events in the 23-week post-intervention period, none were attributed to delayed detection of bacteremia or sepsis. Conclusions: Messaging that ordering providers should order fewer cultures is overly simplistic with consideration for the BMT patient population, yet diagnostic stewardship is essential to optimizing patient experience and outcomes. Attention to new clinical instability among BMT patients is important in detecting bacteremia. Stable symptoms in continuously observed BMT inpatients are unlikely to represent bacteremia.

While conducting archaeological survey to document the large prehistoric canal systems in the central portion of the Tehuacán Valley, investigators recorded a mound and plaza complex that includes what appears to be an effigy mound in the shape of a scorpion. Large quantities of ceramics, including surface-decorated and polychromes, indicate a Late Classic and Postclassic occupation. The site is interpreted as being part of an intensive agricultural system as it appears centrally located in the context of highly developed agricultural and irrigation infrastructure. For the reasons described, we interpret this ca. 60 meter scorpion effigy mound as an intentional feature with possible astronomical alignments. It is hypothesized as being part of a local civic/ceremonial complex with the possible use/function of observing the summer and winter solstices. If so, it provides an insight into the integration of calendrical ritual with the surrounding complex system of fields and irrigation canals. Admittedly, these observations and explanations are relatively subjective. However, we consider them to be persuasive when the evidence is considered in its entirety.

Bifactor Item Response Theory (IRT) models are the usual option for modeling composite constructs. However, in application, researchers typically must assume that all dimensions of person parameter space are orthogonal. This can result in absurd model interpretations. We propose a new bifactor model—the Completely Oblique Rasch Bifactor (CORB) model—which allows for estimation of correlations between all dimensions. We discuss relations of this model to other oblique bifactor models and study the conditions for its identification in the dichotomous case. We analytically prove that this model is identified in the case that (a) at least one item loads solely on the general factor and no items are shared between any pair of specific factors (we call this the G-structure), or (b) if no items load solely on the general factor, but at least one item is shared between every pair of the specific factors (the S-structure). Using simulated and real data, we show that this model outperforms the other partially oblique bifactor models in terms of model fit because it corresponds to the more realistic assumptions about construct structure. We also discuss possible difficulties in the interpretation of the CORB model’s parameters using, by analogy, the “explaining away” phenomenon from Bayesian reasoning.

In this paper, I will review some aspects of psychometric projects that I have been involved in, emphasizing the nature of the work of the psychometricians involved, especially the balance between the statistical and scientific elements of that work. The intent is to seek to understand where psychometrics, as a discipline, has been and where it might be headed, in part at least, by considering one particular journey (my own). In contemplating this, I also look to psychometrics journals to see how psychometricians represent themselves to themselves, and in a complementary way, look to substantive journals to see how psychometrics is represented there (or perhaps, not represented, as the case may be). I present a series of questions in order to consider the issue of what are the appropriate foci of the psychometric discipline. As an example, I present one recent project at the end, where the roles of the psychometricians and the substantive researchers have had to become intertwined in order to make satisfactory progress. In the conclusion I discuss the consequences of such a view for the future of psychometrics.

Item response theory models posit latent variables to account for regularities in students' performances on test items. Wilson's “Saltus” model extends the ideas of IRT to development that occurs in stages, where expected changes can be discontinuous, show different patterns for different types of items, or even exhibit reversals in probabilities of success on certain tasks. Examples include Piagetian stages of psychological development and Siegler's rule-based learning. This paper derives marginal maximum likelihood (MML) estimation equations for the structural parameters of the Saltus model and suggests a computing approximation based on the EM algorithm. For individual examinees, empirical Bayes probabilities of learning-stage are given, along with proficiency parameter estimates conditional on stage membership. The MML solution is illustrated with simulated data and an example from the domain of mixed number subtraction.

This paper discusses the application of a class of Rasch models to situations where test items are grouped into subsets and the common attributes of items within these subsets brings into question the usual assumption of conditional independence. The models are all expressed as particular cases of the random coefficients multinomial logit model developed by Adams and Wilson. This formulation allows a very flexible approach to the specification of alternative models, and makes model testing particularly straightforward. The use of the models is illustrated using item bundles constructed in the framework of the SOLO taxonomy of Biggs and Collis.

A category where the frequency of responses is zero, either for sampling or structural reasons, will be called a null category. One approach for ordered polytomous item response models is to downcode the categories (i.e., reduce the score of each category above the null category by one), thus altering the relationship between the substantive framework and the scoring scheme for items with null categories. It is discussed why this is often not a good idea, and a method for avoiding the problem is described for the partial credit model while maintaining the integrity of the original response framework. This solution is based on a simple reexpression of the basic parameters of the model.