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The aim of this study was to investigate the effect of scanning origin location on the data accuracy of removable partial denture (RPD) abutment teeth region in digital impressions acquired by an intraoral scanner. A mandibular partially edentulous model including the following target abutment teeth was used: the left second molar (#37); left first premolar (#34); and right second premolar (#45). The following scanning strategies were tested: the strategy starting from #37 to mesial direction (37M); strategies starting from #34 to mesial (34M) and distal directions (34D), and strategies starting from #45 to mesial (45M) and distal directions (45D). The evaluated measures were trueness, precision, and linear accuracy. One-way and two-way ANOVA were performed for the comparison of trueness and linear accuracy, while Kruskal–Wallis test was performed for the precision comparison (α = 0.05). 45M and 45D showed significantly superior trueness of #34 to 37M and 34D. 45M also showed significantly superior trueness of #45 to 34. 45D showed significantly inferior linear accuracy of #34 and superior linear accuracy of #45 compared to other strategies. It was concluded that scanning origin location would have an impact on data accuracy of RPD abutment teeth region in digital impressions acquired by intraoral scanner.

The objective of the study was to identify the critical factors that determine recommendations and other decisions about healthcare-related tests and diagnostic strategies (HCTDS). We used a qualitative descriptive approach and conducted semi-structured in-depth interviews with 24 international experts (informants) in evidence and decisions about HCTDS. Although test accuracy (TA) was the factor most commonly considered by organizations when developing recommendations about HCTDS, informants agreed that TA is necessary but rarely, if ever, sufficient and may be misleading when solely considered. The informants identified factors that are important for developing recommendations about HCTDS. Informants largely agreed that laying out the potential care pathways based on the test result is an essential early step but is rarely done in developing recommendations about HCTDS. Most informants also agreed that decision analysis could be useful for organizing the clinical, cost, and preference data relevant to the use of tests in the absence of direct evidence. However, they noted that using models is limited by the lack of resources and expertise required. Developing guidelines about HCTDS requires consideration of factors beyond TA, but implementing this may be challenging. Further development and testing of “frameworks” that can guide this process is a priority for decision makers.

Background/Objectives: Accurate dietary assessment is crucial for nutritional epidemiology, but tools like 24 h recalls (24HRs) face challenges with missing or implausible data. The Automated Self-Administered 24 h Dietary Assessment Tool (ASA24) facilitates large-scale data collection, but its lack of interviewer input may lead to implausible dietary recalls (IDRs), affecting data integrity. Multiple imputation (MI) is commonly used to handle missing data, but its effectiveness in high-variability dietary data is uncertain. This study aims to assess MI’s accuracy in estimating nutrient intake under varying levels of missing data. Methods: Data from 24HRs completed by 743 adolescents (ages 13–18) in Ontario, Canada, were used. Implausible recalls were excluded based on nutrient thresholds, creating a cleaned reference dataset. Missing data were simulated at 10%, 20%, and 40% deletion rates. MI via chained equations was applied, incorporating demographic and psychosocial variables as predictors. Imputed values were compared to actual values using Spearman’s correlation and accuracy within ±10% of true values. Results: Spearman’s rho values between the imputed and actual nutrient intakes were weak (mean ρ ≈ 0.24). Accuracy within ±10% was low for most nutrients (typically < 25%), with no clear trend by missingness level. Diet quality scores showed slightly higher accuracy, but values were still under 30%. Conclusions: MI performed poorly in estimating individual nutrient intake in this adolescent sample. While MI may preserve sample characteristics, it is unreliable for accurate nutrient estimates and should be used cautiously. Future studies should focus on improving data quality and exploring better imputation methods.

Background: Patients use Web-based medical information to understand medical conditions and treatments. A number of efforts have been made to understand the quality of professionally created content; however, none have described the quality of advice being provided between anonymous members of Web-based message boards. Objective: The objective of this study was to characterize the quality of medical information provided between members of an anonymous internet message board addressing treatment with an implantable cardioverter-defibrillator (ICD). Methods: We quantitatively analyzed 2 years of discussions using a mixed inductive-deductive framework, first, for instances in which members provided medical advice and, then, for the quality of the advice. Results: We identified 82 instances of medical advice within 127 discussions. Advice covered 6 topical areas: (1) Device information, (2) Programming, (3) Cardiovascular disease, (4) Lead management, (5) Activity restriction, and (6) Management of other conditions. Across all advice, 50% (41/82) was deemed generally appropriate, 24% (20/82) inappropriate for most patients, 6% (5/82) controversial, and 20% (16/82) without sufficient context. Proportions of quality categories varied between topical areas. We have included representative examples. Conclusions: The quality of advice shared between anonymous members of a message board regarding ICDs varied considerably according to topical area and the specificity of advice. This report provides a model to describe the quality of the available Web-based patient-generated material.

This article provides updated GRADE guidance about how authors of systematic reviews and health technology assessments and guideline developers can rate the certainty of evidence (also known as quality of the evidence or confidence in the estimates) of a body of evidence addressing test accuracy (TA) on the domains imprecision, inconsistency, publication bias, and other domains. It also provides guidance for how to present synthesized information in evidence profiles and summary of findings tables. We present guidance for rating certainty in TA in clinical and public health and review the presentation of results of a body of evidence regarding tests. Supplemented by practical examples, we describe how raters of the evidence can apply the GRADE domains inconsistency, imprecision, and publication bias to a body of evidence of TA studies. Using GRADE in Cochrane and other reviews as well as World Health Organization and other guidelines helped refining the GRADE approach for rating the certainty of a body of evidence from TA studies. Although several of the GRADE domains (e.g., imprecision and magnitude of the association) require further methodological research to help operationalize them, judgments need to be made on the basis of what is known so far.

This article provides updated GRADE guidance about how authors of systematic reviews and health technology assessments and guideline developers can assess the results and the certainty of evidence (also known as quality of the evidence or confidence in the estimates) of a body of evidence addressing test accuracy (TA). We present an overview of the GRADE approach and guidance for rating certainty in TA in clinical and public health and review the presentation of results of a body of evidence regarding tests. Part 1 of the two parts in this 21st guidance article about how to apply GRADE focuses on understanding study design issues in test accuracy, provide an overview of the domains, and describe risk of bias and indirectness specifically. Supplemented by practical examples, we describe how raters of the evidence using GRADE can evaluate study designs focusing on tests and how they apply the GRADE domains risk of bias and indirectness to a body of evidence of TA studies. Rating the certainty of a body of evidence using GRADE in Cochrane and other reviews and World Health Organization and other guidelines dealing with in TA studies helped refining our approach. The resulting guidance will help applying GRADE successfully for questions and recommendations focusing on tests.

With the proliferation of online data collection in human-subjects research, concerns have been raised over the presence of inattentive survey participants and non-human respondents (bots). We compared the quality of the data collected through five commonly used platforms. Data quality was indicated by the percentage of participants who meaningfully respond to the researcher’s question (high quality) versus those who only contribute noise (low quality). We found that compared to MTurk, Qualtrics, or an undergraduate student sample (i.e., SONA), participants on Prolific and CloudResearch were more likely to pass various attention checks, provide meaningful answers, follow instructions, remember previously presented information, have a unique IP address and geolocation, and work slowly enough to be able to read all the items. We divided the samples into high- and low-quality respondents and computed the cost we paid per high-quality respondent. Prolific ( $1.90) and CloudResearch ($ 2.00) were cheaper than MTurk ( $4.36) and Qualtrics ($ 8.17). SONA cost $0.00, yet took the longest to collect the data.

Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis are purely associative, identifying diseases that are strongly correlated with a patients symptoms. We show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive counterfactual diagnostic algorithms. We compare our counterfactual algorithms to the standard associative algorithm and 44 doctors using a test set of clinical vignettes. While the associative algorithm achieves an accuracy placing in the top 48% of doctors in our cohort, our counterfactual algorithm places in the top 25% of doctors, achieving expert clinical accuracy. Our results show that causal reasoning is a vital missing ingredient for applying machine learning to medical diagnosis. In medical diagnosis a doctor aims to explain a patient’s symptoms by determining the diseases causing them, while existing diagnostic algorithms are purely associative. Here, the authors reformulate diagnosis as a counterfactual inference task and derive new counterfactual diagnostic algorithms.

Incomplete reporting has been identified as a major source of avoidable waste in biomedical research. Essential information is often not provided in study reports, impeding the identification, critical appraisal, and replication of studies. To improve the quality of reporting of diagnostic accuracy studies, the Standards for Reporting Diagnostic Accuracy (STARD) statement was developed. Here we present STARD 2015, an updated list of 30 essential items that should be included in every report of a diagnostic accuracy study. This update incorporates recent evidence about sources of bias and variability in diagnostic accuracy and is intended to facilitate the use of STARD. As such, STARD 2015 may help to improve completeness and transparency in reporting of diagnostic accuracy studies.

Background Malaria remains a major health concern in Nigeria. Rapid diagnostic tests (RDTs) are widely used in health facilities to confirm malaria before treatment. However, concerns remain about healthcare workers (HCWs) adherence to, and reporting of test results. This study assessed the accuracy of RDT results recorded in health facility registers in two states of Nigeria by comparing them with an unbiased reference standard and explored factors influencing interrater agreement. Methods A mixed-method evaluation was conducted in 16 health facilities across Oyo and Sokoto States. RDTs performed by HCWs were photographed using a digital RDT reader and independently re-interpreted by a trained, independent, objective panel. Surveys of health facilities and HCWs collected data on factors that could influence RDT recording. Interrater agreement between RDT results recorded by HCWs in facility registers and the external panel was assessed using Cohen’s kappa. A meta-analytical approach was used to calculate a pooled summary kappa value across facilities, and potential moderators of agreement were examined, including characteristics of facilities, HCWs and RDTs. Results Out of 19,586 RDTs captured, 18,319 were included in the analysis. Overall, 6.2% of RDTs were misrecorded as positive and 3.7% as negative in health facility registers, yielding a positive predictive value of 87.2% (95% confidence interval [CI] 86.4%, 87.8%) and negative predictive value of 92.9%. The overall percentage agreement was 90.2% (95% CI 89.7%, 90.6%), and the pooled kappa statistic was 0.80 (95% CI 0.75, 0.85), indicating strong agreement. However, kappa values varied substantially across facilities (range: 0.59, 0.92). Lower agreement was observed in facilities in Sokoto State and in areas with lower malaria prevalence and test positivity. Faint test lines, found in 8.8% of RDTs, were associated with a significantly increased likelihood of results misrecorded as negative. HCWs were more likely to misrecord RDT results as positive when a malaria diagnosis or antimalarial prescription had been made. Conclusion While overall agreement between facility registers and panel-interpreted RDT results was strong, the proportion of results misrecorded as positive and negative highlight the need for improved training, supportive supervision, and mechanisms to promote accurate RDT interpretation and recording. Targeted interventions are essential to ensure the reliability of routine malaria data and support national control efforts.