Faculty Bibliography
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Investigating the analytical robustness of the social and behavioural sciences
The same dataset can be analysed in different justifiable ways to answer the same research question, potentially challenging the robustness of empirical science1-3. In this crowd initiative, we investigated the degree to which research findings in the social and behavioural sciences are contingent on analysts' choices. We examined a stratified random sample of 100 studies published between 2009 and 2018, in which, for one claim per study, at least five reanalysts independently reanalysed the original data. The statistical appropriateness of the reanalyses was assessed in peer evaluations, and the robustness indicators were inspected along a range of research characteristics and study designs. We found that 34% of the independent reanalyses yielded the same result (within a tolerance region of ±0.05 Cohen's d) as the original report; with a four times broader tolerance region, this indicator increased to 57%. Of the reanalyses conducted, 74% reached the same conclusion as the original investigation, 24% yielded no effects or inconclusive results and 2% reported the opposite effect. This exploratory study indicates that the common single-path analyses in social and behavioural research should not be simply assumed to be robust to alternative analyses4. Therefore, we recommend the development and use of practices to explore and communicate this neglected source of uncertainty.
PMID: 41922703
ISSN: 1476-4687
CID: 6041172
Eponyms in Dentistry - Prosthodontics [Historical Article]
This article highlights the significance of 20 dental eponyms in prosthodontics, emphasizing the enduring legacy of the individuals behind them. Each name represents a pivotal advancement and a foundational contribution to the history of dentistry and prosthodontics in particular. Understanding their lives and innovations fosters a deeper appreciation of today's clinical practice, which are built on past discoveries. As current technologies become tomorrow's historical artifacts, recognizing the evolution of the field helps contextualize modern dentistry and anticipate future directions. Honoring these pioneers is essential to preserving the continuity of knowledge and valuing the individual efforts that have shaped the profession.
PMID: 41926373
ISSN: 1089-6287
CID: 6041232
Global, regional, and national burden of meningitis, its risk factors, and aetiologies, 1990-2023: a systematic analysis for the Global Burden of Disease Study 2023
BACKGROUND:Meningitis remains the leading infectious cause of neurological disabilities globally, disproportionately affecting children younger than 5 years and populations in the African meningitis belt. Whereas previous global estimates focused on ten pathogen categories, this study presents the most comprehensive analysis to date, assessing the meningitis burden attributable to 17 causative pathogens based on the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 framework. METHODS:GBD is a systematic, scientific effort aimed at quantifying the comparative magnitude of health loss caused by diseases, injuries, and risk factors across age groups, sexes, and geographical locations over time. We estimated meningitis mortality using the Cause of Death Ensemble model (CODEm) and morbidity using DisMod-MR 2.1, incorporating data from vital registration, verbal autopsy, surveillance, hospital data, and systematic reviews. Aetiology-specific estimates were generated with pathogen-linked case-fatality ratios and splined binomial regression models. Risk factor attribution was based on established risk-outcome pairs and population attributable fractions. FINDINGS/RESULTS:In 2023, there were 259 000 (95% uncertainty interval 202 000-335 000) global deaths and 2·54 million (2·20-2·93) incident cases of meningitis. Children younger than 5 years accounted for more than a third of deaths (86 600 [53 300-149 000]). Streptococcus pneumoniae, Neisseria meningitidis, non-polio enteroviruses, and other viruses were the leading causes of death, while non-polio enteroviruses caused the most cases. The four WHO-defined preventable meningitis pathogens of interest (S pneumoniae, N meningitidis, Haemophilus influenzae, and Group B streptococcus) contributed to 98 700 deaths (77 000-127 000) and 594 000 cases (514 000-686 000). Low birthweight, short gestation, and household air pollution were the top risk factors for meningitis-related mortality. INTERPRETATION/CONCLUSIONS:Although mortality and incidence have declined significantly since 1990, progress is insufficient to meet WHO 2030 targets. Despite marked progress in reducing bacterial meningitis via global vaccination campaigns, a substantial meningitis burden persists, attributable both to common pathogens such as S pneumoniae and N meningitidis and to emerging non-bacterial pathogens such as Candida spp and drug-resistant fungi. Achieving WHO goals will require sustained investment in surveillance, vaccination, maternal screening, and health-system strengthening, especially in high-burden settings. FUNDING/BACKGROUND:Gates Foundation, Wellcome Trust, and UK Department of Health and Social Care.
PMID: 41911930
ISSN: 1474-4465
CID: 6041152
Mobile Imaging-Based Machine Learning for Dental Caries, Sealants, and Fluorosis: Protocol for a Cross-Sectional Model Development and Validation Study
BACKGROUND:Assessing dental caries, sealants, and fluorosis is essential for public health surveillance, providing critical data to evaluate national prevention programs. Standard methods performed by dental professionals are often limited by affordability, accessibility, and scalability for both population-level and individualized assessments. Mobile health (mHealth) approaches to concurrently detect caries, sealants, and fluorosis have remained largely unexplored, especially at the population level. OBJECTIVE:This study leverages mHealth technologies that integrate computer vision using machine learning and deep learning with images captured by smartphone cameras and low-cost intraoral cameras. The primary objective is to develop and validate models for detecting caries lesions, identifying sealants, and quantifying fluorosis severity from standardized dental images, using standardized visual clinical examinations as the reference standard. METHODS:The proposed study population will include approximately 1000 adolescents in Colorado, United States, living in communities with naturally elevated fluoride levels in the public water system. Participants will undergo standardized clinical dental examinations and imaging using intraoral cameras and smartphones. Supervised learning models will incorporate reference chart-based color correction, radiomic spatial and textural features, and neural network classifiers. The reference standard will be standardized visual clinical examinations performed by trained and calibrated dental professionals. Two models will be developed and evaluated: one to detect caries lesions and sealants and another to assess fluorosis severity. Model performance will be evaluated against clinical assessments by dental professionals using stratified cross-validation and multiclass performance metrics while minimizing bias and accounting for confounders common to human examiners. RESULTS:A standardized dental examination, an intraoral imaging protocol, and a smartphone imaging protocol are used to assess all 8 permanent molars for caries and sealants, as well as the 6 upper anterior teeth for fluorosis severity. Pilot studies were conducted to test study logistics and calibrate 3 examiners in person, supplemented by debriefings, mobile app training, and a web-based calibration module. The study was funded in September 2022 with supplemental funding awarded in June 2024. The study launched in May 2024, and as of January 2026, data have been collected from approximately 300 participants. CONCLUSIONS:The integration of computer vision and mobile device imaging will enable affordable, scalable, population-level assessments for detecting caries and sealants and quantifying fluorosis severity among adolescents. mHealth technologies have been increasingly incorporated into dentistry for both clinical decision support and at-home use. This protocol will further help establish a structured methodological framework for acquiring, processing, and analyzing mobile imaging data for dental health surveillance and epidemiological studies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)/UNASSIGNED:DERR1-10.2196/91239.
PMCID:13077280
PMID: 41911013
ISSN: 1929-0748
CID: 6041142
AI Methods for Implementation Science (AIM-IS): developing a framework, toolkit, and reporting standard for the responsible use of AI in implementation practice and research
BACKGROUND:Artificial intelligence (AI), including machine learning, natural language processing, and large language models, may support implementation practice and research in tasks such as evidence synthesis, determinant assessment, strategy selection, monitoring, adaptation, and theory development. However, these applications of AI do not form a single, uniform category. They span a continuum from practice-facing applications that support local implementation work to research- and methods-facing applications that support evidence generation and synthesis. The guidance on how to classify, evaluate, and report these uses of AI remains limited. The AI Methods for Implementation Science (AIM-IS) program aims to develop, validate, and maintain a suite of products to guide the responsible use of AI across implementation practice, implementation research, and bridging use cases. METHODS:AIM-IS is a multi-phase, multi-method methodological development program. The unit of analysis is the AI-for-implementation use case: a specific AI capability supporting a defined implementation practice or research task within a workflow, decision point, and governance context. Phase 1 is a living scoping review mapping published AI use cases in implementation science, including how they are evaluated and what risks they raise. Phase 2 is a qualitative interview study with implementation researchers, practitioners, AI experts, community members, and data infrastructure and governance experts to refine use cases and identify feasibility constraints, outcome priorities, and reporting needs. Phase 3 will integrate findings from Phases 1 and 2 to develop the draft AIM-IS products, including a framework, a taxonomy of use cases, guardrails for responsible use, a practical guide, outcome domains, and reporting items. Phase 4 will use an eDelphi process and consensus meeting to refine and finalize these products. Phase 5 will conduct usability testing to improve clarity and ease of use, resulting in the finalized AIM-IS products. AIM-IS is informed by implementation science, sociotechnical systems, equity, and responsible AI frameworks, and includes a living-update approach to support ongoing refinement. DISCUSSION/CONCLUSIONS:The AIM-IS program will deliver a suite of products, including a framework, toolkit and reporting standard, to support the specification, governance, evaluation, and reporting of AI in implementation science. Together, these products aim to strengthen transparency, comparability, accountability, and attention to equity in how AI is used by implementation practitioners and researchers over time. REGISTRATION/BACKGROUND:Open Science Framework, March 15, 2026: https://doi.org/10.17605/OSF.IO/BX35K.
PMCID:13192042
PMID: 41975498
ISSN: 1748-5908
CID: 6041292
Large-scale exome analyses reveal new rare variant contributions in amyotrophic lateral sclerosis
Amyotrophic lateral sclerosis (ALS) is a heritable disorder where rare variants with low-to-moderate penetrance are thought to dominate genetic risk. To identify such rare variants, we harmonized and analyzed exome data from 22 cohorts, totaling 17,919 individuals with ALS and 200,703 controls across discovery and replication phases. Rare variant analyses identified several new risk genes, with replication confirming association of YKT6 and supporting HTR3C, GBGT1 and KNTC1. We also provide strong, independent validation for genes with limited previous evidence: ARPP21, DNAJC7 and CFAP410. Notably, in ARPP21, we identified a new high-effect variant (p.P747L) and confirmed that p.P563L is an ALS-associated variant leading to an aggressive disease course. Beyond new discoveries, our analyses largely recapitulated the known genetic architecture of ALS, identifying risk variants in over 20% of cases and supporting a cumulative oligogenic risk model. These findings highlight new translational targets and show that rare variant analyses capture substantially more genetic risk than common variant genome-wide association studies.
PMCID:13083253
PMID: 41917433
ISSN: 1546-1718
CID: 6041162
Eponyms in Dentistry - Oral Surgery [Historical Article]
The development of oral and maxillofacial surgery advanced rapidly following the introduction of general anesthesia in 1846, evolving from simple tooth extractions to complex procedures involving the jaws and facial skeleton. Initially driven by general surgeons in Europe and later refined by specialists in the U.S. and elsewhere, this progress is reflected in enduring surgical eponyms. This paper highlights 23 pioneers associated with 21 foundational eponyms-procedures, instruments, and classifications-that remain central to oral surgical practice. These eponyms honor the innovators whose work shaped the field and continue to connect modern surgery to its historical roots.
PMID: 41926370
ISSN: 1089-6287
CID: 6041202
Eponyms in Dentistry - Physiology and Pathology [Historical Article]
Do you ever wonder who is behind the names, diseases, structures, procedures, or syndromes often taught in dental or medical school? For instance, the Cusp of Carabelli on a maxillary molar, the Wharton duct of the submandibular gland, or the Eustachian tube that gives the perception of a stuffed ear before landing are three structures named after individuals who first described them centuries ago. This is a long-overdue exploration of 60 names for 53 of the most relevant eponyms, many of whom have likely been forgotten.I.
PMID: 41926368
ISSN: 1089-6287
CID: 6041182
Authors' Response [Letter]
PMID: 41931070
ISSN: 1943-4723
CID: 6041262
Eponyms in Dentistry - Anatomy and Histology [Historical Article]
This article, the second in a series of eight, highlights the lives and original works of 21 scientists whose names are preserved in 20 enduring eponyms still found in dental anatomy and histology textbooks. Though frequently referenced in education, the historical context and the original publications behind these terms are often overlooked. By revisiting their biographies and citing the original sources where each eponym was first described, this work offers a long-overdue acknowledgment of their lasting contributions to dental science.
PMID: 41926369
ISSN: 1089-6287
CID: 6041192
