A New Hierarchy of Research Evidence for Tumor Pathology: A Delphi Study to Define Levels of Evidence in Tumor Pathology

The hierarchy of evidence is a fundamental concept in evidence-based medicine, but existing models can be challenging to apply in laboratory-based health care disciplines, such as pathology, where the types of evidence and contexts are significantly different from interventional medicine. This project aimed to define a comprehensive and complementary framework of new levels of evidence for evaluating research in tumor pathology-introducing a novel Hierarchy of Research Evidence for Tumor Pathology collaboratively designed by pathologists with help from epidemiologists, public health professionals, oncologists, and scientists, specifically tailored for use by pathologists-and to aid in the production of the World Health Organization Classification of Tumors (WCT) evidence gap maps. To achieve this, we adopted a modified Delphi approach, encompassing iterative online surveys, expert oversight, and external peer review, to establish the criteria for evidence in tumor pathology, determine the optimal structure for the new hierarchy, and ascertain the levels of confidence for each type of evidence. Over a span of 4 months and 3 survey rounds, we collected 1104 survey responses, culminating in a 3-day hybrid meeting in 2023, where a new hierarchy was unanimously agreed upon. The hierarchy is organized into 5 research theme groupings closely aligned with the subheadings of the WCT, and it consists of 5 levels of evidence-level P1 representing evidence types that merit the greatest level of confidence and level P5 reflecting the greatest risk of bias. For the first time, an international collaboration of pathology experts, supported by the International Agency for Research on Cancer, has successfully united to establish a standardized approach for evaluating evidence in tumor pathology. We intend to implement this novel Hierarchy of Research Evidence for Tumor Pathology to map the available evidence, thereby enriching and informing the WCT effectively.

Machine learning computational tools to assist the performance of systematic reviews: A mapping review

BACKGROUND

Within evidence-based practice (EBP), systematic reviews (SR) are considered the highest level of evidence in that they summarize the best available research and describe the progress in a determined field. Due its methodology, SR require significant time and resources to be performed; they also require repetitive steps that may introduce biases and human errors. Machine learning (ML) algorithms therefore present a promising alternative and a potential game changer to speed up and automate the SR process. This review aims to map the current availability of computational tools that use ML techniques to assist in the performance of SR, and to support authors in the selection of the right software for the performance of evidence synthesis.

METHODS

The mapping review was based on comprehensive searches in electronic databases and software repositories to obtain relevant literature and records, followed by screening for eligibility based on titles, abstracts, and full text by two reviewers. The data extraction consisted of listing and extracting the name and basic characteristics of the included tools, for example a tool's applicability to the various SR stages, pricing options, open-source availability, and type of software. These tools were classified and graphically represented to facilitate the description of our findings.

RESULTS

A total of 9653 studies and 585 records were obtained from the structured searches performed on selected bibliometric databases and software repositories respectively. After screening, a total of 119 descriptions from publications and records allowed us to identify 63 tools that assist the SR process using ML techniques.

CONCLUSIONS

This review provides a high-quality map of currently available ML software to assist the performance of SR. ML algorithms are arguably one of the best techniques at present for the automation of SR. The most promising tools were easily accessible and included a high number of user-friendly features permitting the automation of SR and other kinds of evidence synthesis reviews.

[Biliary stenosis in patients treated with liver transplantation. Diagnostic approach]

Between April 1986 and August 1994, 393 orthotopic liver transplantation (OLT) have been performed at "12 de Octubre" Hospital. Among these ones we consider 274 OLT made in 223 adults and in 47 children (4 intraoperative deaths). The reconstruction of the biliary tract was performed with a choledocho-choledochostomy with T tube (CD-CD T) in 131 patients, a choledocho-choledochostomy without T tube or stent (CD-CD) in 75, a Roux-en-y-hepatico-jejunostomy (H-J) in 248, a hepatico-jejunostomy with stent (H-J St) in 13 and a choledocho-cholecisto-jejunostomy (CD-CC-J) in 3 patients. Thirthy six (13.3%) patients developed biliary complications (30 adults and 6 childrens). Fourteen (18.6%) occurred in CD-CD reconstruction and 13 (11.4%) in CD-CD T. The most common complications were leakage and stricture. Thirteen ERCP were performed in 12 patients (1 failed), all adults (CD-CD T: 3; CD-CD: 10). The main indication for ERCP was cholestasis and inability of non invasive methods ultrasound, scintigraphy and computerized tomography in determining the underlying etiology. ERCP was successful in all 12 patients: detecting strictures in 8, strictures + lithiasis in 1, stricture+lekage in 1 and leakage in 2. No complications were encountered after ERCP in our patients. ERCP is the method of choice in diagnosis of biliary complications in CD-CD biliary reconstruction.