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BackgroundChatbots are artificial intelligence–driven programs that interact with people. The applications of this technology include the collection and delivery of information, generation of and responding to inquiries, collection of end user feedback, and the delivery of personalized health and medical information to patients through cellphone- and web-based platforms. However, no chatbots have been developed for patients with lung cancer and their caregivers. ObjectiveThis study aimed to develop and evaluate the early feasibility of a chatbot designed to improve the knowledge of symptom management among patients with lung cancer in Japan and their caregivers. MethodsWe conducted a sequential mixed methods study that included a web-based anonymized questionnaire survey administered to physicians and paramedics from June to July 2019 (phase 1). Two physicians conducted a content analysis of the questionnaire to curate frequently asked questions (FAQs; phase 2). Based on these FAQs, we developed and integrated a chatbot into a social network service (phase 3). The physicians and paramedics involved in phase I then tested this chatbot (α test; phase 4). Thereafter, patients with lung cancer and their caregivers tested this chatbot (β test; phase 5). ResultsWe obtained 246 questions from 15 health care providers in phase 1. We curated 91 FAQs and their corresponding responses in phase 2. In total, 11 patients and 1 caregiver participated in the β test in phase 5. The participants were asked 60 questions, 8 (13%) of which did not match the appropriate categories. After the β test, 7 (64%) participants responded to the postexperimental questionnaire. The mean satisfaction score was 2.7 (SD 0.5) points out of 5. ConclusionsMedical staff providing care to patients with lung cancer can use the categories specified in this chatbot to educate patients on how they can manage their symptoms. Further studies are required to improve chatbots in terms of interaction with patients.

There are currently no abstract classifiers, which can be used for new diagnostic test accuracy (DTA) systematic reviews to select primary DTA study abstracts from database searches. Our goal was to develop machine-learning-based abstract classifiers for new DTA systematic reviews through an open competition. We prepared a dataset of abstracts obtained through database searches from 11 reviews in different clinical areas. As the reference standard, we used the abstract lists that required manual full-text review. We randomly splitted the datasets into a train set, a public test set, and a private test set. Competition participants used the training set to develop classifiers and validated their classifiers using the public test set. The classifiers were refined based on the performance of the public test set. They could submit as many times as they wanted during the competition. Finally, we used the private test set to rank the submitted classifiers. To reduce false exclusions, we used the Fbeta measure with a beta set to seven for evaluating classifiers. After the competition, we conducted the external validation using a dataset from a cardiology DTA review. We received 13,774 submissions from 1429 teams or persons over 4 months. The top-honored classifier achieved a Fbeta score of 0.4036 and a recall of 0.2352 in the external validation. In conclusion, we were unable to develop an abstract classifier with sufficient recall for immediate application to new DTA systematic reviews. Further studies are needed to update and validate classifiers with datasets from other clinical areas.

It is often challenging to present the available evidence in a timely and comprehensible manner. We aimed to visualize the evolution of evidence about antidepressants for depression by conducting cumulative network meta-analyses (NMAs) and to examine whether it could have helped the selection of optimal drugs. We built a Shiny web application that performs and presents cumulative NMAs based on R netmeta. We used a comprehensive dataset of double-blind randomized controlled trials of 21 antidepressants in the acute treatment of major depression. The primary outcomes were efficacy (treatment response) and acceptability (all-cause discontinuation), and treatment effects were summarized via odds ratios. We evaluated the confidence in evidence using the CINeMA (Confidence in Network Meta-Analysis) framework for a series of consecutive NMAs. Users can change several conditions for the analysis, such as the period of synthesis, among the others. We present the league tables and two-dimensional plots that combine efficacy, acceptability and level of confidence in the evidence together, for NMAs conducted in 1990, 1995, 2000, 2005, 2010, and 2016. They reveal that through the past four decades, newly approved drugs often showed initially exaggerated results, which tended to diminish and stabilize after approximately a decade. Over the years, the drugs with relative superiority changed dramatically; but as the evidence network grew larger and better connected, the overall confidence improved. The Shiny app visualizes how evidence evolved over years, emphasizing the need for a careful interpretation of relative effects between drugs, especially for the potentially amplified performance of newly approved drugs.

Objectives: Lymphovascular invasion (LVI) is a critical risk factor for lymph node metastasis (LNM), which requires additional surgery after endoscopic resection of T1 colorectal cancer (CRC). However, the impact of additional staining on estimating LNM is unclear. This systematic review aimed to evaluate the impact of additional staining on determining LNM in T1 CRC. Methods: We searched five electronic databases. Outcomes were diagnostic odds ratio (DOR), assessed using hierarchical summary receiver operating characteristic curves, and interobserver agreement among pathologists for positive LVI, assessed using Kappa coefficients (κ). We performed a subgroup analysis of studies that simultaneously included a multivariable analysis for other risk factors (deep submucosal invasion, poor differentiation, and tumor budding). Results: Among the 64 studies (18,097 patients) identified, hematoxylin–eosin (HE) and additional staining for LVI had pooled sensitivities of 0.45 (95% confidence interval [CI] 0.32–0.58) and 0.68 (95% CI 0.44–0.86), specificities of 0.88 (95% CI 0.78–0.94) and 0.76 (95% CI 0.62–0.86), and DORs of 6.26 (95% CI 3.73–10.53) and 6.47 (95% CI 3.40–12.32) for determining LNM, respectively. In multivariable analysis, the DOR of additional staining for LNM (DOR 5.95; 95% CI 2.87–12.33) was higher than that of HE staining (DOR 1.89; 95% CI 1.13–3.16) (P = 0.01). Pooled κ values were 0.37 (95% CI 0.22–0.52) and 0.62 (95% CI 0.04–0.99) for HE and additional staining for LVI, respectively. Conclusion: Additional staining for LVI may increase the DOR for LNM and interobserver agreement for positive LVI among pathologists. [ABSTRACT FROM AUTHOR]

Background and Aims: Relapse is common in alcohol dependence (AD) and alcohol use disorder (AUD), so alcohol reduction therapy should be measured over as long a period as possible; however, existing reviews do not consider the duration of treatment and therefore alcohol reduction therapy may not have been appropriately evaluated. This review evaluated the efficacy and safety of alcohol reduction pharmacotherapy in patients with AD or AUD according to the duration of treatment. Methods: We conducted a systematic review and network meta‐analysis of randomized controlled trials (RCTs) that assessed 15 pharmacological agents. MEDLINE, Embase, PsycINFO, the Cochrane Central Register of Controlled Trials, the ClinicalTrials.gov and the International Clinical Trials Registry Platform were searched for eligible trials through to May 2021. Outcomes were heavy drinking days (HDD), total alcohol consumption (TAC), any adverse event and days without drinking. Results: Fifty‐five RCTs (n = 8891) were included. Nalmefene was superior to placebo for reducing HDD (standard mean difference [SMD] −0.28, 95% confidence interval [CI] −0.37, −0.18) and TAC (SMD −0.25, 95% CI −0.35, −0.16) in the long‐term, but not in the short‐term. Topiramate was superior to placebo for reducing HDD (SMD −0.35, 95% CI −0.59, −0.12) and days without drinking (SMD 0.46, 95% CI 0.11, 0.82), and baclofen was superior for reducing TAC (SMD −0.70, 95% CI −1.29, −0.11), in the short‐term. The frequency of adverse events was higher with nalmefene and topiramate than with placebo. Conclusion: Nalmefene, topiramate and baclofen may be effective as alcohol reduction pharmacotherapy; however, only nalmefene has demonstrated long‐term efficacy, and nalmefene and topiramate have a significantly higher frequency of adverse events compared with placebo. [ABSTRACT FROM AUTHOR]