OBJECTIVE: The fragility index (FI), which is the minimum number of changes in status from \"event\" to \"non-event\" resulting in a loss of statistical significance, serves as a significant supplementary indicator for clinical physicians in interpreting clinical trial results and aids in understanding the outcomes of randomized controlled trials (RCTs). In this systematic literature survey, we evaluated the FI for RCTs evaluating Chinese herbal medicine (CHM) for irritable bowel syndrome (IBS), and explored potential associations between study characteristics and the robustness of RCTs.
METHODS: A comprehensive search was conducted in four databases in Chinese and four databases in English from their inception to January 1, 2023. RCTs encompassed 1:1 ratio into two parallel arms and reported at least one binary outcome that demonstrated statistical significance were included. FI was calculated by the iterative reduction of a target outcome event in the treatment group and concomitant subtraction of a non-target event from that group, until positive significance (defined as P < 0.05 by Fisher\'s exact test) is lost. The lower the FI (minimum 1) of a trial outcome, the more fragile the positive result of the outcome was. Linear regression models were adopted to explore influence factors of the value of FI.
RESULTS: A total of 30 trials from 2 4118 potentially relevant citations were finally included. The median FI of total trials included was 1.5 (interquartile range [IQR], 1-5), and half of the trials (n = 15) had a FI equal to 1. In 12 trials (40%), the total number of participants lost to follow-up surpassed the respective FI. The study also identified that increased FI was significantly associated with no TCM syndrome differentiation for inclusion criteria of the patients, larger total sample size, low risk of bias, and larger numbers of events.
CONCLUSIONS: The majority of CHM IBS RCTs with positive results were found to be fragile. Ensuring adequate sample size, scientifically rigorous study design, proper control of confounding factors, and a quality control calibration for consistency of TCM diagnostic results among clinicians should be addressed to increase the robustness of the RCTs. We recommend reporting the FI as one of the components of sensitivity analysis in future RCTs to facilitate the assessment of the fragility of trials.

Oncologists are faced with choosing the best treatment for each patient, based on the available evidence from randomized controlled trials (RCTs) and observational studies. RCTs provide estimates of the average effects of treatments on groups of patients, but they may not apply in many real-world scenarios where for example patients have different characteristics than the RCT participants, or where different treatment variants are considered. Causal inference defines what a treatment effect is and how it may be estimated with RCTs or outside of RCTs with observational - or \'real-world\' - data. In this review, we introduce the field of causal inference, explain what a treatment effect is and what important challenges are with treatment effect estimation with observational data. We then provide a framework for conducting causal inference studies and describe when in oncology causal inference from observational data may be particularly valuable. Recognizing the strengths and limitations of both RCTs and observational causal inference provides a way for more informed and individualized treatment decision-making in oncology.

Periodontitis is independently associated with numerous lifestyle diseases. Diabetic patients have approximately threefold increased odds of periodontitis, which in turn increases the risk of systemic inflammation. The study by Thazhe Poyil et al is an effort to establish the inflammatory link between diabetic re-tinopathy (DR) and periodontitis based on the periodontal inflamed surface area in diabetic patients with and without DR. To further advance the study, we suggest refining the eligibility criteria to explicitly state the clinical correlates of periodontitis and DR, larger sample size and improved sampling methodology, matching of baseline characteristics of the two groups, as well as improved statistical approach and interpretation of the study findings. Measurement of hemoglobin A1c (HbA1c) in studies comparing type 2 diabetes mellitus patients with DR of matched severity with and without periodontitis could provide a clearer picture of whether HbA1c level is indeed influenced by periodontitis.

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The objective of this article is to illustrate the importance of the role philosophy and its tools play in the designing and undertaking of nursing research and its importance to be understood by the practicing researcher to ensure the selected methodology and the tools used provide the framework for obtaining reliable and valid answers to their research questions. The article discusses the three major paradigms (Positivism, Interpretivism, and Pragmatism) which have characterized much of health-related research together with a set of essential and practical tools with no metaphysical assumptions that will hopefully provide an explicit framework and a nomenclature which can be applied as we proceed through the research process.

Despite clearly established guidelines, recent audits have found the conduct and reporting of systematic reviews and meta-analyses (SRMAs) within neurosurgery to be relatively lackluster in methodological rigor and compliance. Protocols of SRMAs allow for planning and documentation of review methods, guard against arbitrary decision-making during the review process, and enable readers to assess for the presence of selective reporting. To aid transparency, authors should provide sufficient detail in their protocol so that the readers could reproduce the study themselves. Development of our guideline drew heavily from the Preferred Reporting Items for Systematic Reviews and Meta-analyses Protocols (PRISMA-P) initiative. The objective of this article is not to enumerate every detail of this checklist, but to provide guidance to authors preparing their protocol, with examples, for a systematic review in neurosurgery. Particularly, we emphasize on the PICO framework - population (P), interventions (I), comparators (C), outcomes (O) - which is central to constructing a clinical question, defining the scope of the systematic review, defining and prioritizing the primary outcome, to specifying the eligibility criteria, designing the search strategy, and identifying potential sources of heterogeneity. We encourage our readers to make use of this guideline alongside the PRISMA-P 2015 statement, when drafting and appraising systematic review protocols.

Neurosurgeons are inundated with the Herculean task to keep abreast with the rapid pace at which clinical research is proliferating. Systematic reviews and meta-analyses (SRMAs) have consequently surged in popularity because when executed properly, they constitute the highest level of evidence, and may save busy neurosurgeons many hours of combing the literature. Well-executed SRMAs may prove instructive for clinical practice, but poorly conducted reviews sow confusion and may potentially cause harm. Unfortunately, many SRMAs within neurosurgery are relatively lackluster in methodological rigor. When neurosurgeons apply the results of an SRMA to patient care, they should start by evaluating the extent to which the employed methods have likely protected against misleading results. The present article aims to educate the reader about how to interpret an SRMA, to assess the potential relevance of its results in the special context of the neurosurgical patient population.

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BACKGROUND: In-depth interviews are a common method of qualitative data collection, providing rich data on individuals\' perceptions and behaviors that would be challenging to collect with quantitative methods. Researchers typically need to decide on sample size a priori. Although studies have assessed when saturation has been achieved, there is no agreement on the minimum number of interviews needed to achieve saturation. To date, most research on saturation has been based on in-person data collection. During the COVID-19 pandemic, web-based data collection became increasingly common, as traditional in-person data collection was possible. Researchers continue to use web-based data collection methods post the COVID-19 emergency, making it important to assess whether findings around saturation differ for in-person versus web-based interviews.
OBJECTIVE: We aimed to identify the number of web-based interviews needed to achieve true code saturation or near code saturation.
METHODS: The analyses for this study were based on data from 5 Food and Drug Administration-funded studies conducted through web-based platforms with patients with underlying medical conditions or with health care providers who provide primary or specialty care to patients. We extracted code- and interview-specific data and examined the data summaries to determine when true saturation or near saturation was reached.
RESULTS: The sample size used in the 5 studies ranged from 30 to 70 interviews. True saturation was reached after 91% to 100% (n=30-67) of planned interviews, whereas near saturation was reached after 33% to 60% (n=15-23) of planned interviews. Studies that relied heavily on deductive coding and studies that had a more structured interview guide reached both true saturation and near saturation sooner. We also examined the types of codes applied after near saturation had been reached. In 4 of the 5 studies, most of these codes represented previously established core concepts or themes. Codes representing newly identified concepts, other or miscellaneous responses (eg, \"in general\"), uncertainty or confusion (eg, \"don\'t know\"), or categorization for analysis (eg, correct as compared with incorrect) were less commonly applied after near saturation had been reached.
CONCLUSIONS: This study provides support that near saturation may be a sufficient measure to target and that conducting additional interviews after that point may result in diminishing returns. Factors to consider in determining how many interviews to conduct include the structure and type of questions included in the interview guide, the coding structure, and the population under study. Studies with less structured interview guides, studies that rely heavily on inductive coding and analytic techniques, and studies that include populations that may be less knowledgeable about the topics discussed may require a larger sample size to reach an acceptable level of saturation. Our findings also build on previous studies looking at saturation for in-person data collection conducted at a small number of sites.

At a time when developments in computational approaches, often associated with the now much-vaunted terms Machine Learning (ML) and Artificial Intelligence (AI), face increasing challenges in terms of fairness, transparency and accountability, the temptation for researchers to apply mainstream ML methods to virtually any type of data seems to remain irresistible. In this paper we critically examine a recent proposal to apply ML to polygraph screening results (where human interviewers have made a conclusion about deception), which raises several questions about the purpose and the design of the research, particularly given the vacuous scientific status of polygraph-based procedures themselves. We argue that in high-stake environments such as criminal justice and employment practice, where fundamental rights and principles of justice are at stake, the legal and ethical considerations for scientific research are heightened. Specifically, we argue that the combination of ambiguously labelled data and ad hoc ML models does not meet this requirement. Worse, such research can inappropriately legitimise otherwise scientifically invalid, indeed pseudo-scientific methods such as polygraph-based deception detection, especially when presented in a reputable scientific journal. We conclude that methodological concerns, such as those highlighted in this paper, should be addressed before research can be said to contribute to resolving any of the fundamental validity issues that underlie methods and techniques used in legal proceedings.