Performance of spatial capture-recapture models with repurposed data: Assessing estimator robustness for retrospective applications.

Advancements in statistical ecology offer the opportunity to gain further inferences from existing data with minimal financial cost. Spatial capture-recapture (SCR) models extend traditional capture-recapture models to incorporate spatial position of capture and enable direct estimation of animal densities across a region of interest. The additional inferences provided are both ecologically interesting and valuable for decision making, which has resulted in traditional capture-recapture data being repurposed using SCR. Yet, many capture-recapture studies were not designed for SCR and the limitations of repurposing data from such studies are rarely assessed in practice. We used simulation to evaluate the robustness of SCR for retrospectively estimating large mammal densities over a variety of scenarios using repurposed capture-recapture data collected by an asymmetrical sampling grid and covering a broad spatial extent in a heterogenous landscape. We found performance of SCR models fit using repurposed data simulated from the existing grid was not robust, but instead bias and precision of density estimates varied considerably among simulations scenarios. For example, while the smallest relatives bias of density estimates was 3%, it ranged by 14 orders of magnitude among scenarios and was most strongly influenced by detection parameters. Our results caution against the casual repurposing of non-spatial capture-recapture data using SCR and demonstrate the importance of using simulation to assessing model performance during retrospective applications.

Factors that impact turnaround time of surgical pathology specimens in an academic institution.

Turnaround time of laboratory results is important for customer satisfaction. The College of American Pathologists' checklist requires an analytic turnaround time of 2 days or less for most routine cases and lets every hospital define what a routine specimen is. The objective of this study was to analyze which factors impact turnaround time of nonbiopsy surgical pathology specimens. We calculated the turnaround time from receipt to verification of results (adjusted for weekends and holidays) for all nonbiopsy surgical specimens during a 2-week period. Factors studied included tissue type, number of slides per case, decalcification, immunohistochemistry, consultations with other pathologists, and diagnosis. Univariate and multivariate analyses were performed. A total of 713 specimens were analyzed, 551 (77%) were verified within 2 days and 162 (23%) in 3 days or more. Lung, gastrointestinal, breast, and genitourinary specimens showed the highest percentage of cases being signed out in over 3 days. Diagnosis of malignancy (including staging of the neoplasia), consultation with other pathologists, having had a frozen section, and use of immunohistochemical stains were significantly associated with increased turnaround time in univariate analysis. Decalcification was not associated with increased turnaround time. In multivariate analysis, consultation with other pathologists, use of immunohistochemistry, diagnosis of malignancy, and the number of slides studied continued to be significantly associated with prolonged turnaround time. Our findings suggest that diagnosis of malignancy is central to significantly prolonging the turnaround time for surgical pathology specimens, thus institutions that serve cancer centers will have longer turnaround time than those that do not.