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BMC Medical Education Jan 2024Adequacy of learning models and their ability to engage students and match session's objectives are critical factors in achieving the desired outcome. In this systematic... (Meta-Analysis)
Meta-Analysis
BACKGROUND
Adequacy of learning models and their ability to engage students and match session's objectives are critical factors in achieving the desired outcome. In this systematic review and meta-analysis, we assess the methodological approach, content, and effectiveness of training initiatives addressing medical students' knowledge, attitudes, confidence and discrimination perception towards LGBTQIA + people.
METHOD
PubMed, Web of Science, Medline and Scopus were searched to identify published studies, from 2013 to 2023, on effectiveness of training initiatives addressing medical students' knowledge, attitudes, confidence and discrimination perception towards LGBTQIA + people. The risk of bias of the selected studies was assessed by the Medical Education Research Study Quality Instrument. Overall effect sizes were calculated using a Mantel-Haenszel method, fixed effect meta-analyses.
RESULTS
A total of 22 studies were included, representing 2,164 medical students. The interventions were highly diverse and included seminars, lectures, videos, real-case discussions, roleplay, and group discussions with people from the LGBTQIA + community. After the interventions, there was a significant improvement in self-confidence and comfort interacting with patients and in the understanding of the unique and specific health concerns experienced by LGBTQIA + patients.
CONCLUSION
Our findings indicated that the outcomes of interventions training actions for medical students that promote knowledge and equity regarding LGBTQIA + people, regardless of their scope, methodology and duration, result in a considerable increase in students' self-confidence and comfort interacting with LGBTQIA + patients, highlight the need for more actions and programs in this area promoting a more inclusive society and greater equity.
Topics: Humans; Students, Medical; Sexual and Gender Minorities; Learning; Attitude; Education, Medical
PubMed: 38229060
DOI: 10.1186/s12909-024-05041-w -
Cancers Dec 2023To better define the overall performance of the current radiomics-based models for the discrimination of pediatric posterior fossa tumors. (Review)
Review
PURPOSE
To better define the overall performance of the current radiomics-based models for the discrimination of pediatric posterior fossa tumors.
METHODS
A comprehensive literature search of the databases PubMed, Ovid MEDLINE, Ovid EMBASE, Web of Science, and Scopus was designed and conducted by an experienced librarian. We estimated overall sensitivity (SEN) and specificity (SPE). Event rates were pooled across studies using a random-effects meta-analysis, and the χ test was performed to assess the heterogeneity.
RESULTS
Overall SEN and SPE for differentiation between MB, PA, and EP were found to be promising, with SEN values of 93% (95% CI = 0.88-0.96), 83% (95% CI = 0.66-0.93), and 85% (95% CI = 0.71-0.93), and corresponding SPE values of 87% (95% CI = 0.82-0.90), 95% (95% CI = 0.90-0.98) and 90% (95% CI = 0.84-0.94), respectively. For MB, there is a better trend for LR classifiers, while textural features are the most used and the best performing (ACC 96%). As for PA and EP, a synergistic employment of LR and NN classifiers, accompanied by geometrical or morphological features, demonstrated superior performance (ACC 94% and 96%, respectively).
CONCLUSIONS
The diagnostic performance is high, making radiomics a helpful method to discriminate these tumor types. In the forthcoming years, we expect even more precise models.
PubMed: 38136435
DOI: 10.3390/cancers15245891 -
American Journal of Medicine Open Dec 2023To systematically review contemporary prediction models for hospital mortality developed or validated in general medical patients.
OBJECTIVE
To systematically review contemporary prediction models for hospital mortality developed or validated in general medical patients.
METHODS
We screened articles in five databases, from January 1, 2010, through April 7, 2022, and the bibliography of articles selected for final inclusion. We assessed the quality for risk of bias and applicability using the Prediction Model Risk of Bias Assessment Tool (PROBAST) and extracted data using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist. Two investigators independently screened each article, assessed quality, and extracted data.
RESULTS
From 20,424 unique articles, we identified 15 models in 8 studies across 10 countries. The studies included 280,793 general medical patients and 19,923 hospital deaths. Models included 7 early warning scores, 2 comorbidities indices, and 6 combination models. Ten models were studied in all general medical patients (general models) and 7 in general medical patients with infection (infection models). Of the 15 models, 13 were developed using logistic or Poisson regression and 2 using machine learning methods. Also, 4 of 15 models reported on handling of missing values. None of the infection models had high discrimination, whereas 4 of 10 general models had high discrimination (area under curve >0.8). Only 1 model appropriately assessed calibration. All models had high risk of bias; 4 of 10 general models and 5 of 7 infection models had low concern for applicability for general medical patients.
CONCLUSION
Mortality prediction models for general medical patients were sparse and differed in quality, applicability, and discrimination. These models require hospital-level validation and/or recalibration in general medical patients to guide mortality reduction interventions.
PubMed: 38090393
DOI: 10.1016/j.ajmo.2023.100044 -
JVS-vascular Science 2023Restenosis is a significant complication of revascularization treatments in coronary and peripheral arteries, sometimes necessitating repeated intervention. Establishing... (Review)
Review
OBJECTIVE
Restenosis is a significant complication of revascularization treatments in coronary and peripheral arteries, sometimes necessitating repeated intervention. Establishing when restenosis will happen is extremely difficult due to the interplay of multiple variables and factors. Standard clinical and Doppler ultrasound scans surveillance follow-ups are the only tools clinicians can rely on to monitor intervention outcomes. However, implementing efficient surveillance programs is hindered by health care system limitations, patients' comorbidities, and compliance. Predictive models classifying patients according to their risk of developing restenosis over a specific period will allow the development of tailored surveillance, prevention programs, and efficient clinical workflows. This review aims to: (1) summarize the state-of-the-art in predictive models for restenosis in coronary and peripheral arteries; (2) compare their performance in terms of predictive power; and (3) provide an outlook for potentially improved predictive models.
METHODS
We carried out a comprehensive literature review by accessing the PubMed/MEDLINE database according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search strategy consisted of a combination of keywords and included studies focusing on predictive models of restenosis published between January 1993 and April 2023. One author independently screened titles and abstracts and checked for eligibility. The rest of the authors independently confirmed and discussed in case of any disagreement. The search of published literature identified 22 studies providing two perspectives-clinical and biomechanical engineering-on restenosis and comprising distinct methodologies, predictors, and study designs. We compared predictive models' performance on discrimination and calibration aspects. We reported the performance of models simulating reocclusion progression, evaluated by comparison with clinical images.
RESULTS
Clinical perspective studies consider only routinely collected patient information as restenosis predictors. Our review reveals that clinical models adopting traditional statistics (n = 14) exhibit only modest predictive power. The latter improves when machine learning algorithms (n = 4) are employed. The logistic regression models of the biomechanical engineering perspective (n = 2) show enhanced predictive power when hemodynamic descriptors linked to restenosis are fused with a limited set of clinical risk factors. Biomechanical engineering studies simulating restenosis progression (n = 2) are able to capture its evolution but are computationally expensive and lack risk scoring for individual patients at specific follow-ups.
CONCLUSIONS
Restenosis predictive models, based solely on routine clinical risk factors and using classical statistics, inadequately predict the occurrence of restenosis. Risk stratification models with increased predictive power can be potentially built by adopting machine learning techniques and incorporating critical information regarding vessel hemodynamics arising from biomechanical engineering analyses.
PubMed: 38023962
DOI: 10.1016/j.jvssci.2023.100128 -
Frontiers in Oncology 2023The role of cranial radiation therapy with hippocampus avoidance (HA-CRT) in neurocognitive function (NCF), brain metastasis (BM), and overall survival (OS) in lung...
BACKGROUND
The role of cranial radiation therapy with hippocampus avoidance (HA-CRT) in neurocognitive function (NCF), brain metastasis (BM), and overall survival (OS) in lung cancer remains unclear.
METHODS
A meta-analysis was conducted to evaluate the impact of HA-CRT in lung cancer. Data from studies on hippocampal-avoidance prophylactic cranial irradiation (HA-PCI) and whole brain radiotherapy (HA-WBRT) were pooled.
RESULTS
A total of 14 studies, including 5 randomized controlled trials, were included. The focus of NCF was mainly the Hopkins Verbal Learning Test-Revised or the Free and Cued Selective Reminding Test. At 6 months post-radiotherapy, the pooled proportion of participants with decline in the performance of total recall, delayed recall, and discrimination in neurocognitive tests were 0.22 (95% CI 0.15, 0.29), 0.20 (95% CI 0.13, 0.27), and 0.14 (95% CI 0.05, 0.24) respectively. After 12 months, the proportion were 0.16 (95% CI 0.08, 0.23), 0.10 (95% CI 0.04, 0.16), and 0.04 (95% CI 0, 0.09) respectively. For HA zone relapse, the RR of HA-CRT versus CRT was 2.72 (95% CI 0.53, 13.87), and for 2-year BM, it was 1.20 (95% CI 0.82, 1.75). Regarding HA-PCI in SCLC, the 1-year BM rate was 0.12 (95% CI 0.07, 0.17), and the 2-year BM rate was 0.20 (95% CI 0.16, 0.25). For HA-WBRT in NSCLC with BM, the 2-year intracranial progression rate was 0.38 (95% CI 0.13, 0.62). There was no significant difference in OS between HA-CRT and CRT.
CONCLUSIONS
HA-CRT appears to be safe in lung cancer, but it may not outperform conventional CRT. Larger RCTs comparing HA-CRT and CRT are warranted.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022360890, identifier CRD42022360890.
PubMed: 37936606
DOI: 10.3389/fonc.2023.1268754 -
Cuadernos de Bioetica : Revista Oficial... 2023As health-related big data research (HRBDR) has drastically increased over the last years due to the rapid development of big data analytics, a range of important...
As health-related big data research (HRBDR) has drastically increased over the last years due to the rapid development of big data analytics, a range of important ethical issues are raised. In this study, a systematic literature review was conducted. Several and interesting results emerged from this review. The term ″big data″ has not yet been clearly defined. The already existing ethical principles and concepts need to be revisited in the new HRBDR context. Traditional research ethics notions like privacy and informed consent are to be reconsidered. HRBDR creates new ethical issues such those related to trust / trustworthiness and public values such as reciprocity, transparency, inclusivity and common good. The implementation of dynamic consent rather than broad consent is currently highlighted as the more satisfying solution. Ethical review committees in their current form are ill-suited to provide exclusive ethical oversight on HRBDR projects. Expanding Ethical Review Committees' purview and members' expertise, as well as creating novel oversight bodies by promoting a co-governance system including public and all the stakeholders involved are strongly recommended. The mechanism of ″social licence″, that is, informal permissions granted to researchers by society, can serve as a guideline. High-stakes decisions are often made under uncertainty. Machine learning algorithms are highly complex and in some cases opaque, and may yield biased decisions or discrimination. Improved interdisciplinary dialogue along with considering aspects like auditing, benchmarking, confidence / trust and explainability /interpretability may address concerns about HRBDR ethics. Finally and most importantly, research ethics shifts towards a population-based model of ethics.
Topics: Big Data; Ethics Committees, Research; Informed Consent; Ethics, Research; Ethical Review
PubMed: 37804492
DOI: 10.30444/CB.153 -
Journal of Biomedical Informatics Oct 2023To review and critically appraise published and preprint reports of prognostic models of in-hospital mortality of patients in the intensive-care unit (ICU) based on... (Review)
Review
OBJECTIVE
To review and critically appraise published and preprint reports of prognostic models of in-hospital mortality of patients in the intensive-care unit (ICU) based on neural representations (embeddings) of clinical notes.
METHODS
PubMed and arXiv were searched up to August 1, 2022. At least two reviewers independently selected the studies that developed a prognostic model of in-hospital mortality of intensive-care patients using free-text represented as embeddings and extracted data using the CHARMS checklist. Risk of bias was assessed using PROBAST. Reporting on the model was assessed with the TRIPOD guideline. To assess the machine learning components that were used in the models, we present a new descriptive framework based on different techniques to represent text and provide predictions from text. The study protocol was registered in the PROSPERO database (CRD42022354602).
RESULTS
Eighteen studies out of 2,825 were included. All studies used the publicly-available MIMIC dataset. Context-independent word embeddings are widely used. Model discrimination was provided by all studies (AUROC 0.75-0.96), but measures of calibration were scarce. Seven studies used both structural clinical variables and notes. Model discrimination improved when adding clinical notes to variables. None of the models was externally validated and often a simple train/test split was used for internal validation. Our critical appraisal demonstrated a high risk of bias in all studies and concerns regarding their applicability in clinical practice.
CONCLUSION
All studies used a neural architecture for prediction and were based on one publicly available dataset. Clinical notes were reported to improve predictive performance when used in addition to only clinical variables. Most studies had methodological, reporting, and applicability issues. We recommend reporting both model discrimination and calibration, using additional data sources, and using more robust evaluation strategies, including prospective and external validation. Finally, sharing data and code is encouraged to improve study reproducibility.
PubMed: 37742782
DOI: 10.1016/j.jbi.2023.104504 -
Journal of Clinical Medicine Aug 2023The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer's disease has developed in recent years. However, this approach is... (Review)
Review
The use of radiomics and artificial intelligence applied for the diagnosis and monitoring of Alzheimer's disease has developed in recent years. However, this approach is not yet completely applicable in clinical practice. The aim of this paper is to provide a systematic analysis of the studies that have included the use of radiomics from different imaging techniques and artificial intelligence for the diagnosis and monitoring of Alzheimer's disease in order to improve the clinical outcomes and quality of life of older patients. A systematic review of the literature was conducted in February 2023, analyzing manuscripts and articles of the last 5 years from the PubMed, Scopus and Embase databases. All studies concerning discrimination among Alzheimer's disease, Mild Cognitive Impairment and healthy older people performing radiomics analysis through machine and deep learning were included. A total of 15 papers were included. The results showed a very good performance of this approach in the differentiating Alzheimer's disease patients-both at the dementia and pre-dementia phases of the disease-from healthy older people. In summary, radiomics and AI can be valuable tools for diagnosing and monitoring the progression of Alzheimer's disease, potentially leading to earlier and more accurate diagnosis and treatment. However, the results reported by this review should be read with great caution, keeping in mind that imaging alone is not enough to identify dementia due to Alzheimer's.
PubMed: 37629474
DOI: 10.3390/jcm12165432 -
Journal of Medical Internet Research Jul 2023Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of... (Review)
Review
BACKGROUND
Digital clinical tools are a new technology that can be used in the screening or diagnosis of obstructive sleep apnea (OSA), notwithstanding the crucial role of polysomnography, the gold standard.
OBJECTIVE
This study aimed to identify, gather, and analyze the most accurate digital tools and smartphone-based health platforms used for OSA screening or diagnosis in the adult population.
METHODS
We performed a comprehensive literature search of PubMed, Scopus, and Web of Science databases for studies evaluating the validity of digital tools in OSA screening or diagnosis until November 2022. The risk of bias was assessed using the Joanna Briggs Institute critical appraisal tool for diagnostic test accuracy studies. The sensitivity, specificity, and area under the curve (AUC) were used as discrimination measures.
RESULTS
We retrieved 1714 articles, 41 (2.39%) of which were included in the study. From these 41 articles, we found 7 (17%) smartphone-based tools, 10 (24%) wearables, 11 (27%) bed or mattress sensors, 5 (12%) nasal airflow devices, and 8 (20%) other sensors that did not fit the previous categories. Only 8 (20%) of the 41 studies performed external validation of the developed tool. Of these, the highest reported values for AUC, sensitivity, and specificity were 0.99, 96%, and 92%, respectively, for a clinical cutoff of apnea-hypopnea index (AHI)≥30. These values correspond to a noncontact audio recorder that records sleep sounds, which are then analyzed by a deep learning technique that automatically detects sleep apnea events, calculates the AHI, and identifies OSA. Looking at the studies that only internally validated their models, the work that reported the highest accuracy measures showed AUC, sensitivity, and specificity values of 1.00, 100%, and 96%, respectively, for a clinical cutoff AHI≥30. It uses the Sonomat-a foam mattress that, aside from recording breath sounds, has pressure sensors that generate voltage when deformed, thus detecting respiratory movements, and uses it to classify OSA events.
CONCLUSIONS
These clinical tools presented promising results with high discrimination measures (best results reached AUC>0.99). However, there is still a need for quality studies comparing the developed tools with the gold standard and validating them in external populations and other environments before they can be used in clinical settings.
TRIAL REGISTRATION
PROSPERO CRD42023387748; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=387748.
Topics: Adult; Humans; Surveys and Questionnaires; Sleep Apnea, Obstructive; Sleep Apnea Syndromes; Sleep; Polysomnography
PubMed: 37494079
DOI: 10.2196/47735 -
Frontiers in Immunology 2023Prophylaxis of postoperative recurrence is an intractable problem for clinicians and patients with Crohn's disease. Prognostic models are effective tools for patient...
BACKGROUND AND AIMS
Prophylaxis of postoperative recurrence is an intractable problem for clinicians and patients with Crohn's disease. Prognostic models are effective tools for patient stratification and personalised management. This systematic review aimed to provide an overview and critically appraise the existing models for predicting postoperative recurrence of Crohn's disease.
METHODS
Systematic retrieval was performed using PubMed and Web of Science in January 2022. Original articles on prognostic models for predicting postoperative recurrence of Crohn's disease were included in the analysis. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment (PROBAST) tool. This study was registered with the International Prospective Register of Systematic Reviews (PROSPERO; number CRD42022311737).
RESULTS
In total, 1948 articles were screened, of which 15 were ultimately considered. Twelve studies developed 15 new prognostic models for Crohn's disease and the other three validated the performance of three existing models. Seven models utilised regression algorithms, six utilised scoring indices, and five utilised machine learning. The area under the receiver operating characteristic curve of the models ranged from 0.51 to 0.97. Six models showed good discrimination, with an area under the receiver operating characteristic curve of >0.80. All models were determined to have a high risk of bias in modelling or analysis, while they were at low risk of applicability concerns.
CONCLUSIONS
Prognostic models have great potential for facilitating the assessment of postoperative recurrence risk in patients with Crohn's disease. Existing prognostic models require further validation regarding their reliability and applicability.
SYSTEMATIC REVIEW REGISTRATION
https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022311737.
Topics: Humans; Crohn Disease; Prognosis; Reproducibility of Results
PubMed: 37457731
DOI: 10.3389/fimmu.2023.1215116