BACKGROUND: There is some evidence for systematic biases and failures of research integrity in the anaesthesia literature. However, the features of problematic trials and effect of editorial selection on these issues have not been well quantified.
METHODS: We analysed 209 randomised controlled trials submitted to Anaesthesia between 8 March 2019 and 31 March 2020. We evaluated the submitted manuscript, registry data and the results of investigations into the integrity of the trial undertaken at the time of submission. Trials were labelled \'concerning\' if failures of research integrity were found, and \'problematic\' if identified issues would have warranted retraction if they had been found after publication. We investigated how \'problematic\' trials were detected, the distribution of p values and the risk of outcome reporting bias and p-hacking. We also investigated whether there were any factors that differed in problematic trials.
RESULTS: We found that false data was the most common reason for a trial to be labelled as \'concerning\', which occurred in 51/62 (82%) cases. We also found that while 195/209 (93%) trials were preregistered, we found adequate registration for only 166/209 (79%) primary outcomes, 100/209 (48%) secondary outcomes and 11/209 (5%) analysis plans. We also found evidence for a step decrease in the frequency of p values > 0.05 compared with p values < 0.05. \'Problematic\' trials were all single-centre and appeared to have fewer authors (incident risk ratio (95%CI) 0.8 (0.7-0.9)), but could not otherwise be distinguished reliably from other trials.
CONCLUSIONS: Identification of \'problematic\' trials is frequently dependent on individual patient data, which is often unavailable after publication. Additionally, there is evidence of a risk of outcome reporting bias and p-hacking in submitted trials. Implementation of alternative research and editorial practices could reduce the risk of bias and make identification of problematic trials easier.

UNASSIGNED: It has been recognized that HIV-related stigma hinders efforts in testing, treatment, and prevention. In this systematic review, we aimed to summarize available findings on the association between HIV-related stigma and age, social support, educational status, depression, employment status, wealth index, gender, residence, knowledge about HIV, marital status, duration since diagnosis, and disclosure status using a large number of studies.
UNASSIGNED: Electronic databases including Scopus, Medline/PubMed, Web of Sciences (WOS), Cochrane Library, Google Scholar, and Open Research Dataset Challenge were systematically searched until 15 April 2023. We included all kinds of HIV-stigma studies, regardless of language, publishing date, or geographic location. The inclusion criteria were met by 40 studies, with a total of 171,627 patients. A mixed-effect model was used to pool estimates and evaluate publication bias, as well as to conduct sensitivity analysis.
UNASSIGNED: Factors such as older age, social support, greater education, higher socioeconomic status, good knowledge of HIV, and longer years of living with HIV significantly lowered the likelihood of HIV-related stigma. Contrarily, factors such as depression, residing in rural areas, female respondents, and non-disclosure of HIV status were significantly associated with a high risk of HIV-related stigma.
UNASSIGNED: To combat systemic HIV-associated stigma, it is crucial to develop wholesome and comprehensive social methods by raising community-level HIV awareness. In addition to activism, local economic development is also crucial for creating thriving communities with a strong social fabric.

In medical research, publication bias (PB) poses great challenges to the conclusions from systematic reviews and meta-analyses. The majority of efforts in methodological research related to classic PB have focused on examining the potential suppression of studies reporting effects close to the null or statistically non-significant results. Such suppression is common, particularly when the study outcome concerns the effectiveness of a new intervention. On the other hand, attention has recently been drawn to the so-called inverse publication bias (IPB) within the evidence synthesis community. It can occur when assessing adverse events because researchers may favor evidence showing a similar safety profile regarding an adverse event between a new intervention and a control group. In comparison to the classic PB, IPB is much less recognized in the current literature; methods designed for classic PB may be inaccurately applied to address IPB, potentially leading to entirely incorrect conclusions. This article aims to provide a collection of accessible methods to assess IPB for adverse events. Specifically, we discuss the relevance and differences between classic PB and IPB. We also demonstrate visual assessment through contour-enhanced funnel plots tailored to adverse events and popular quantitative methods, including Egger\'s regression test, Peters\' regression test, and the trim-and-fill method for such cases. Three real-world examples are presented to illustrate the bias in various scenarios, and the implementations are illustrated with statistical code. We hope this article offers valuable insights for evaluating IPB in future systematic reviews of adverse events.

BACKGROUND: The commonly used frequentist paradigm of null hypothesis statistics testing with its reliance on the p-value and the corresponding notion of \'statistical significance\' has been under ongoing criticism. Misinterpretation and misuse of the p-value have contributed to publication bias, unreliable studies, frequent false positives, fraud and mistrust in results of scientific studies. While p-values themselves are still useful, part of the problem may be the confusion between statistical and clinical significance. In randomised controlled trials of health interventions, this confusion could lead to erroneous conclusions about treatment efficacy, research waste and compromised patient outcomes. The extent to which clinical and statistical significance of published randomised clinical trials do not match is not known. This is a protocol for a methodological study to understand the extent of the problem of disparities between statistical and clinical significance in published clinical trials, and to identify and assess the factors associated with discrepant results in these studies.
METHODS: A methodological survey of published randomised controlled trials is planned. Trials published between 2018 and 2022 and their protocols will be searched and screened for inclusion, with a planned sample size of 500 studies. The reported minimum clinically important difference, the study effect size and confidence intervals will be used to assess clinical importance of trial results. Comparison of statistical significance and clinical importance of the trial results will be used to determine disparity. Data will be analysed to estimate the outcomes, and factors associated with disparate study results will be assessed using logistic regression analysis.
BACKGROUND: Ethical approval for the study has been granted by Stellenbosch University\'s Health Research Ethics Committee. This is part of a larger study towards a PhD in Biostatistics and will be disseminated as a thesis, conference abstract and peer-reviewed manuscript.

Social biases may concentrate the attention of researchers on a small number of well-known molecules/mechanisms leaving others underexplored. In accordance with this view, central to mechanistic toxicology is a narrow range of molecular pathways that are assumed to be involved in a significant part of the responses to toxicity. It is unclear, however, if there are other molecular mechanisms which play an important role in toxicity events but are overlooked by toxicology. To identify overlooked genes sensitive to chemical exposures, we used publicly available databases. First, we used data on the published chemical-gene interactions for 17,338 genes to estimate their sensitivity to chemical exposures. Next, we extracted data on publication numbers per gene for 19,243 human genes from the Find My Understudied Genes database. Thresholds were applied to both datasets using our algorithm to identify chemically sensitive and chemically insensitive genes and well-studied and underexplored genes. A total of 1110 underexplored genes highly sensitive to chemical exposures were used in GSEA and Shiny GO analyses to identify enriched biological categories. The metabolism of fatty acids, amino acids, and glucose were identified as underexplored molecular mechanisms sensitive to chemical exposures. These findings suggest that future effort is needed to uncover the role of xenobiotics in the current epidemics of metabolic diseases.

Stereotype threat is a well-known construct in psychology wherein individuals who belong to a negatively stereotyped demographic group underperform on cognitive or academic tasks due to the detrimental effects of a stereotype. Many psychologists have suggested that stereotype threat may be one of the reasons that some demographic groups are underrepresented in advanced academic programs and STEM fields. However, others have raised concerns about the quality of the stereotype threat research, suggesting that its apparent effects are inflated and that the phenomenon may be an illusion of questionable research practices and publication bias. The purpose of this proposed meta-analysis is to evaluate the existence of stereotype threat by (1) identifying the average effect size of stereotype threat studies in different types of studies, (2), investigating whether publication bias and p-hacking are present in the empirical research on stereotype threat, (3) testing for the influence of theoretical and methodological moderators, (4) assessing the overall quality of the research on stereotype threat, (5) and identifying the average effect in the methodologically strongest studies. This meta-analysis will be limited to studies that report data from African Americans because this population is a theoretically important group in stereotype threat research, and the size of score gaps between the African American and non-stereotyped populations in the United States should make the stereotype threat effect easiest to detect.

BACKGROUND: To evaluate outcomes of low with high intraabdominal pressure during laparoscopic colorectal resection surgery.
METHODS: A systematic search of multiple electronic data sources was conducted, and all studies comparing low with high (standard) intraabdominal pressures were included. Our primary outcomes were post-operative ileus occurrence and return of bowel movement/flatus. The evaluated secondary outcomes included: total operative time, post-operative haemorrhage, anastomotic leak, pneumonia, surgical site infection, overall post-operative complications (categorised by Clavien-Dindo grading), and length of hospital stay. Revman 5.4 was used for data analysis.
RESULTS: Six randomised controlled trials (RCTs) and one observational study with a total of 771 patients (370 surgery at low intraabdominal pressure and 401 at high pressures) were included. There was no statistically significant difference in all the measured outcomes; post-operative ileus [OR 0.80; CI (0.42, 1.52), P = 0.50], time-to-pass flatus [OR -4.31; CI (-12.12, 3.50), P = 0.28], total operative time [OR 0.40; CI (-10.19, 11.00), P = 0.94], post-operative haemorrhage [OR 1.51; CI (0.41, 5.58, P = 0.53], anastomotic leak [OR 1.14; CI (0.26, 4.91), P = 0.86], pneumonia [OR 1.15; CI (0.22, 6.09), P = 0.87], SSI [OR 0.69; CI (0.19, 2.47), P = 0.57], overall post-operative complications [OR 0.82; CI (0.52, 1.30), P = 0.40], Clavien-Dindo grade ≥ 3 [OR 1.27; CI (0.59, 2.77), P = 0.54], and length of hospital stay [OR -0.68; CI (-1.61, 0.24), P = 0.15].
CONCLUSIONS: Low intraabdominal pressure is safe and feasible approach to laparoscopic colorectal resection surgery with non-inferior outcomes to standard or high pressures. More robust and well-powered RCTs are needed to consolidate the potential benefits of low over high pressure intra-abdominal surgery.

OBJECTIVE: To describe the characteristics and publication outcomes of clinical prediction model studies registered on clinicaltrials.gov since 2000.
METHODS: Observational studies registered on clinicaltrials.gov between January 1, 2000, and March 2, 2022, describing the development of a new clinical prediction model or the validation of an existing model for predicting individual-level prognostic or diagnostic risk were analyzed. Eligible clinicaltrials.gov records were classified by modeling study type (development, validation) and the model outcome being predicted (prognostic, diagnostic). Recorded characteristics included study status, sample size information, Medical Subject Headings, and plans to share individual participant data. Publication outcomes were analyzed by linking National Clinical Trial numbers for eligible records with PubMed abstracts.
RESULTS: Nine hundred twenty-eight records were analyzed from a possible 89,896 observational study records. Publications searches found 170 matching peer-reviewed publications for 137 clinicaltrials.gov records. The estimated proportion of records with 1 or more matching publications after accounting for time since study start was 2.8% at 2 years (95% CI: 1.7%, 3.9%), 12.3% at 5 years (9.8% to 14.9%) and 27% at 10 years (23% to 33%). Stratifying records by study start year indicated that publication proportions improved over time. Records tended to prioritize the development of new prediction models over the validation of existing models (76%; 704/928 vs. 24%; 182/928). At the time of download, 27% of records were marked as complete, 35% were still recruiting, and 14.7% had unknown status. Only 7.4% of records stated plans to share individual participant data.
CONCLUSIONS: Published clinical prediction model studies are only a fraction of overall research efforts, with many studies planned but not completed or published. Improving the uptake of study preregistration and follow-up will increase the visibility of planned research. Introducing additional registry features and guidance may improve the identification of clinical prediction model studies posted to clinical registries.

Objectives: Exploring the relationship between the hOGG1 rs1052133 polymorphism and the occurrence of nasopharyngeal carcinoma (NPC). Methods: PubMed, Web of Science, Scopus, CNKI, Wanfangdata, and VIP were used to search for studies and the NOS evaluation scale was used to evaluate the quality. All studies were grouped according to different genotypes. The Cochrane\'s Q test and I2 test were used for heterogeneity evaluations. If heterogeneity was small, the fixed effects model was used, and conversely, the random effects model was used. Publication bias was also detected. P < .05 in all results indicated statistically significant. Results: We ultimately included 6 studies with 2021 NPC patients in the study group and 2375 healthy populations in the control group. After meta-analysis, it was found that the total OR value of the \"Ser/Cys (CG) vs Ser/Ser (CC)\" group was 1.00 (95% CI: 0.85-1.18) and the \"Cys/Cys (GG) vs Ser/Ser (CC)\" group was 1.06 (95% CI: 0.87-1.28). These results were not statistically significant (P > .05). Furthermore, the integrated total OR values of each group were not statistically significant with or without the smoking history, even in other genotype models (Allele, Dominant, Recessive, and Additive) (P > .05). Conclusion: There is no clear correlation between the hOGG1 rs1052133 polymorphism and the occurrence of NPC, even with or without the smoking history.

BACKGROUND: Previous literature has explored the relationship between chronic atrophic gastritis (CAG) and isolated cancers within the upper gastrointestinal cancers; However, an integrative synthesis across the totality of upper gastrointestinal cancers was conspicuously absent. The research objective was to assess the relationship between CAG and the risk of incident upper gastrointestinal cancers, specifically including gastric cancer, oesophageal cancer, and oesophagogastric junction cancer.
METHODS: Rigorous systematic searches were conducted across three major databases, namely PubMed, Embase and Web of Science, encompassing the timeline from database inception until August 10, 2023. We extracted the necessary odds ratio (OR) and their corresponding 95% confidence interval (CI) for subsequent meta-analysis. Statistical analyses were conducted using Stata 17.0 software.
RESULTS: This meta-analysis included a total of 23 articles encompassing 5858 patients diagnosed with upper gastrointestinal cancers. CAG resulted in a statistically significant 4.12-fold elevated risk of incident gastric cancer (OR = 4.12, 95% CI 3.20-5.30). Likewise, CAG was linked to a 2.08-fold increased risk of incident oesophageal cancer (OR = 2.08, 95%CI 1.60-2.72). Intriguingly, a specific correlation was found between CAG and the risk of incident oesophageal squamous cell carcinoma (OR = 2.29, 95%CI 1.77-2.95), while no significant association was detected for oesophageal adenocarcinoma (OR = 0.62, 95%CI 0.17-2.26). Moreover, CAG was correlated with a 2.77-fold heightened risk of oesophagogastric junction cancer (OR = 2.77, 95%CI 2.21-3.46). Notably, for the same type of upper gastrointestinal cancer, it was observed that diagnosing CAG through histological methods was linked to a 33-77% higher risk of developing cancer compared to diagnosing CAG through serological methods.
CONCLUSIONS: This meta-analysis indicated a two- to fourfold increased risk of gastric cancer, oesophageal cancer, and oesophagogastric junction cancer in patients with CAG. Importantly, for the same upper gastrointestinal cancer, the risk of incident cancer was higher when CAG was diagnosed histologically compared to serological diagnosis. Further rigorous study designs are required to explore the impact of CAG diagnosed through both diagnostic methods on the risk of upper gastrointestinal cancers.