Titre : Statistiques comme sujet

Statistiques comme sujet : Questions médicales fréquentes

Termes MeSH sélectionnés :

Diffusion Tensor Imaging
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{ "@type": "MedicalWebPage", "name": "Diagnostic", "headline": "Diagnostic sur Statistiques comme sujet", "description": "Comment les statistiques aident-elles au diagnostic médical ?\nQuel rôle jouent les statistiques dans les tests de dépistage ?\nComment les statistiques influencent-elles les critères de diagnostic ?\nLes statistiques peuvent-elles prédire des maladies ?\nQuel est l'impact des statistiques sur le diagnostic précoce ?", "url": "https://questionsmedicales.fr/mesh/D013223?mesh_terms=Diffusion+Tensor+Imaging&page=3#section-diagnostic" }, { "@type": "MedicalWebPage", "name": "Symptômes", "headline": "Symptômes sur Statistiques comme sujet", "description": "Comment les statistiques aident-elles à identifier des symptômes ?\nLes statistiques peuvent-elles quantifier la gravité des symptômes ?\nQuel est le lien entre statistiques et symptômes rares ?\nComment les statistiques influencent-elles la reconnaissance des symptômes ?\nLes statistiques peuvent-elles aider à l'évaluation des symptômes chroniques ?", "url": "https://questionsmedicales.fr/mesh/D013223?mesh_terms=Diffusion+Tensor+Imaging&page=3#section-symptômes" }, { "@type": "MedicalWebPage", "name": "Prévention", "headline": "Prévention sur Statistiques comme sujet", "description": "Comment les statistiques contribuent-elles à la prévention des maladies ?\nQuel rôle jouent les statistiques dans les campagnes de vaccination ?\nLes statistiques peuvent-elles aider à cibler les populations à risque ?\nComment les statistiques évaluent-elles les programmes de dépistage ?\nQuel est l'impact des statistiques sur les recommandations de santé publique ?", "url": "https://questionsmedicales.fr/mesh/D013223?mesh_terms=Diffusion+Tensor+Imaging&page=3#section-prévention" }, { "@type": "MedicalWebPage", "name": "Traitements", "headline": "Traitements sur Statistiques comme sujet", "description": "Comment les statistiques évaluent-elles l'efficacité des traitements ?\nQuel rôle jouent les statistiques dans les essais cliniques ?\nLes statistiques peuvent-elles prédire la réponse au traitement ?\nComment les statistiques aident-elles à personnaliser les traitements ?\nQuel est l'impact des statistiques sur le suivi des traitements ?", "url": "https://questionsmedicales.fr/mesh/D013223?mesh_terms=Diffusion+Tensor+Imaging&page=3#section-traitements" }, { "@type": "MedicalWebPage", "name": "Complications", "headline": "Complications sur Statistiques comme sujet", "description": "Comment les statistiques aident-elles à identifier les complications ?\nQuel rôle jouent les statistiques dans la gestion des complications ?\nLes statistiques peuvent-elles prédire les complications ?\nComment les statistiques influencent-elles la recherche sur les complications ?\nQuel est l'impact des statistiques sur le suivi des complications ?", "url": "https://questionsmedicales.fr/mesh/D013223?mesh_terms=Diffusion+Tensor+Imaging&page=3#section-complications" }, { "@type": "MedicalWebPage", "name": "Facteurs de risque", "headline": "Facteurs de risque sur Statistiques comme sujet", "description": "Comment les statistiques identifient-elles les facteurs de risque ?\nQuel rôle jouent les statistiques dans l'évaluation des facteurs de risque ?\nLes statistiques peuvent-elles aider à réduire les facteurs de risque ?\nComment les statistiques influencent-elles les recommandations sur les facteurs de risque ?\nQuel est l'impact des statistiques sur la sensibilisation aux facteurs de risque ?", "url": "https://questionsmedicales.fr/mesh/D013223?mesh_terms=Diffusion+Tensor+Imaging&page=3#section-facteurs de risque" } ] }, { "@type": "FAQPage", "mainEntity": [ { "@type": "Question", "name": "Comment les statistiques aident-elles au diagnostic médical ?", "position": 1, "acceptedAnswer": { "@type": "Answer", "text": "Elles permettent d'analyser des données cliniques pour identifier des tendances et des anomalies." } }, { "@type": "Question", "name": "Quel rôle jouent les statistiques dans les tests de dépistage ?", "position": 2, "acceptedAnswer": { "@type": "Answer", "text": "Elles évaluent la sensibilité et la spécificité des tests pour déterminer leur efficacité." } }, { "@type": "Question", "name": "Comment les statistiques influencent-elles les critères de diagnostic ?", "position": 3, "acceptedAnswer": { "@type": "Answer", "text": "Elles aident à établir des seuils basés sur des données probantes pour des maladies spécifiques." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles prédire des maladies ?", "position": 4, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles utilisent des modèles prédictifs basés sur des données épidémiologiques." } }, { "@type": "Question", "name": "Quel est l'impact des statistiques sur le diagnostic précoce ?", "position": 5, "acceptedAnswer": { "@type": "Answer", "text": "Elles permettent d'identifier des biomarqueurs et d'améliorer le dépistage précoce." } }, { "@type": "Question", "name": "Comment les statistiques aident-elles à identifier des symptômes ?", "position": 6, "acceptedAnswer": { "@type": "Answer", "text": "Elles analysent la fréquence et la distribution des symptômes dans des populations." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles quantifier la gravité des symptômes ?", "position": 7, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles mesurent l'intensité des symptômes à l'aide d'échelles validées." } }, { "@type": "Question", "name": "Quel est le lien entre statistiques et symptômes rares ?", "position": 8, "acceptedAnswer": { "@type": "Answer", "text": "Elles aident à caractériser et à comprendre la prévalence des symptômes rares." } }, { "@type": "Question", "name": "Comment les statistiques influencent-elles la reconnaissance des symptômes ?", "position": 9, "acceptedAnswer": { "@type": "Answer", "text": "Elles fournissent des données sur les symptômes les plus courants associés à des maladies." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles aider à l'évaluation des symptômes chroniques ?", "position": 10, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles permettent d'analyser l'impact des symptômes chroniques sur la qualité de vie." } }, { "@type": "Question", "name": "Comment les statistiques contribuent-elles à la prévention des maladies ?", "position": 11, "acceptedAnswer": { "@type": "Answer", "text": "Elles identifient les facteurs de risque et évaluent l'efficacité des interventions préventives." } }, { "@type": "Question", "name": "Quel rôle jouent les statistiques dans les campagnes de vaccination ?", "position": 12, "acceptedAnswer": { "@type": "Answer", "text": "Elles évaluent l'impact des campagnes sur l'incidence des maladies évitables." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles aider à cibler les populations à risque ?", "position": 13, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles analysent les données démographiques pour identifier les groupes vulnérables." } }, { "@type": "Question", "name": "Comment les statistiques évaluent-elles les programmes de dépistage ?", "position": 14, "acceptedAnswer": { "@type": "Answer", "text": "Elles mesurent l'impact des programmes sur la détection précoce et la mortalité." } }, { "@type": "Question", "name": "Quel est l'impact des statistiques sur les recommandations de santé publique ?", "position": 15, "acceptedAnswer": { "@type": "Answer", "text": "Elles fournissent des données probantes pour orienter les politiques de santé publique." } }, { "@type": "Question", "name": "Comment les statistiques évaluent-elles l'efficacité des traitements ?", "position": 16, "acceptedAnswer": { "@type": "Answer", "text": "Elles comparent les résultats cliniques entre groupes traités et non traités." } }, { "@type": "Question", "name": "Quel rôle jouent les statistiques dans les essais cliniques ?", "position": 17, "acceptedAnswer": { "@type": "Answer", "text": "Elles déterminent la taille de l'échantillon et analysent les résultats pour valider les traitements." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles prédire la réponse au traitement ?", "position": 18, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles utilisent des modèles pour identifier les facteurs prédictifs de réponse." } }, { "@type": "Question", "name": "Comment les statistiques aident-elles à personnaliser les traitements ?", "position": 19, "acceptedAnswer": { "@type": "Answer", "text": "Elles analysent les données génétiques et cliniques pour adapter les traitements aux patients." } }, { "@type": "Question", "name": "Quel est l'impact des statistiques sur le suivi des traitements ?", "position": 20, "acceptedAnswer": { "@type": "Answer", "text": "Elles permettent d'évaluer l'adhérence et les effets secondaires des traitements au fil du temps." } }, { "@type": "Question", "name": "Comment les statistiques aident-elles à identifier les complications ?", "position": 21, "acceptedAnswer": { "@type": "Answer", "text": "Elles analysent la fréquence des complications dans les études cliniques et épidémiologiques." } }, { "@type": "Question", "name": "Quel rôle jouent les statistiques dans la gestion des complications ?", "position": 22, "acceptedAnswer": { "@type": "Answer", "text": "Elles évaluent les résultats des traitements pour minimiser les complications post-opératoires." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles prédire les complications ?", "position": 23, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles utilisent des modèles pour identifier les facteurs de risque de complications." } }, { "@type": "Question", "name": "Comment les statistiques influencent-elles la recherche sur les complications ?", "position": 24, "acceptedAnswer": { "@type": "Answer", "text": "Elles fournissent des données pour orienter les recherches sur la prévention des complications." } }, { "@type": "Question", "name": "Quel est l'impact des statistiques sur le suivi des complications ?", "position": 25, "acceptedAnswer": { "@type": "Answer", "text": "Elles permettent d'évaluer l'évolution des complications et d'ajuster les traitements." } }, { "@type": "Question", "name": "Comment les statistiques identifient-elles les facteurs de risque ?", "position": 26, "acceptedAnswer": { "@type": "Answer", "text": "Elles analysent les données épidémiologiques pour établir des associations entre facteurs et maladies." } }, { "@type": "Question", "name": "Quel rôle jouent les statistiques dans l'évaluation des facteurs de risque ?", "position": 27, "acceptedAnswer": { "@type": "Answer", "text": "Elles quantifient l'impact des facteurs de risque sur l'incidence des maladies." } }, { "@type": "Question", "name": "Les statistiques peuvent-elles aider à réduire les facteurs de risque ?", "position": 28, "acceptedAnswer": { "@type": "Answer", "text": "Oui, elles évaluent l'efficacité des interventions pour réduire les comportements à risque." } }, { "@type": "Question", "name": "Comment les statistiques influencent-elles les recommandations sur les facteurs de risque ?", "position": 29, "acceptedAnswer": { "@type": "Answer", "text": "Elles fournissent des données probantes pour orienter les conseils de santé publique." } }, { "@type": "Question", "name": "Quel est l'impact des statistiques sur la sensibilisation aux facteurs de risque ?", "position": 30, "acceptedAnswer": { "@type": "Answer", "text": "Elles aident à communiquer les risques aux populations pour promouvoir des comportements sains." } } ] } ] }

Sous-catégories

64 au total
└─

Analyse actuarielle

Actuarial Analysis D000206 - N06.850.520.830.100
└─

Analyse de variance

Analysis of Variance D000704 - N06.850.520.830.150
└─

Aire sous la courbe

Area Under Curve D019540 - N06.850.520.830.200
└─

Analyse de regroupements

Cluster Analysis D016000 - N06.850.520.830.250
└─

Intervalles de confiance

Confidence Intervals D016001 - N06.850.520.830.275
└─

Interprétation statistique de données

Data Interpretation, Statistical D003627 - N06.850.520.830.300
└─

Analyse discriminante

Discriminant Analysis D016002 - N06.850.520.830.350
└─

Utilisation des installations et des services

Facilities and Services Utilization D000078336 - N06.850.520.830.375
└─

Analyse statistique factorielle

Factor Analysis, Statistical D005163 - N06.850.520.830.400
└─

Analyse appariée

Matched-Pair Analysis D016555 - N06.850.520.830.475
└─

Modèles statistiques

Models, Statistical D015233 - N06.850.520.830.500
└─

Méthode de Monte Carlo

Monte Carlo Method D009010 - N06.850.520.830.525
└─

Analyse multiniveaux

Multilevel Analysis D055361 - N06.850.520.830.562
└─

Probabilité

Probability D011336 - N06.850.520.830.600
└─

Utilisation des procédures et des techniques

Procedures and Techniques Utilization D000078337 - N06.850.520.830.675
└─

Analyse de régression

Regression Analysis D012044 - N06.850.520.830.750
└─

Sensibilité et spécificité

Sensitivity and Specificity D012680 - N06.850.520.830.872
└─

Analyse spatiale

Spatial Analysis D062206 - N06.850.520.830.933
└─

Lois statistiques

Statistical Distributions D016008 - N06.850.520.830.994
└─

Statistique non paramétrique

Statistics, Nonparametric D018709 - N06.850.520.830.995
└─

Processus stochastiques

Stochastic Processes D013269 - N06.850.520.830.996
└─

Analyse de survie

Survival Analysis D016019 - N06.850.520.830.998
└─└─

Analyse multifactorielle

Multivariate Analysis D015999 - N06.850.520.830.150.500
└─└─

Analyse de structure latente

Latent Class Analysis D000077272 - N06.850.520.830.250.338
└─└─

Analyse de positionnement multidimensionnel

Multidimensional Scaling Analysis D000084142 - N06.850.520.830.250.507
└─└─

Analyse en petite superficie

Small-Area Analysis D017062 - N06.850.520.830.250.675
└─└─

Analyses secondaires des données

Secondary Data Analysis D000094422 - N06.850.520.830.300.500
└─└─

Analyse de médiation

Mediation Analysis D000081983 - N06.850.520.830.400.500
└─└─

Modèles économiques

Models, Economic D018803 - N06.850.520.830.500.600
└─└─

Nomogrammes

Nomograms D049451 - N06.850.520.830.500.625
└─└─

Théorème de Bayes

Bayes Theorem D001499 - N06.850.520.830.600.200
└─└─

Fonctions de vraisemblance

Likelihood Functions D016013 - N06.850.520.830.600.400
└─└─

Odds ratio

Odds Ratio D016017 - N06.850.520.830.600.600
└─└─

Score de propension

Propensity Score D057216 - N06.850.520.830.600.650
└─└─

Risque

Risk D012306 - N06.850.520.830.600.800
└─└─

Incertitude

Uncertainty D035501 - N06.850.520.830.600.900
└─└─

Méthode des moindres carrés

Least-Squares Analysis D016018 - N06.850.520.830.750.400
└─└─

Modèles linéaires

Linear Models D016014 - N06.850.520.830.750.425
└─└─

Modèles logistiques

Logistic Models D016015 - N06.850.520.830.750.450
└─└─

Limite de détection

Limit of Detection D057230 - N06.850.520.830.872.500
└─└─

Rapport signal-bruit

Signal-To-Noise Ratio D059629 - N06.850.520.830.872.750
└─└─

Cartographie géographique

Geographic Mapping D062305 - N06.850.520.830.933.249
└─└─

Régression spatiale

Spatial Regression D000068916 - N06.850.520.830.933.375
└─└─

Analyse spatio-temporelle

Spatio-Temporal Analysis D062211 - N06.850.520.830.933.500
└─└─

Loi binomiale

Binomial Distribution D016010 - N06.850.520.830.994.250
└─└─

Loi du khi-deux

Chi-Square Distribution D016009 - N06.850.520.830.994.300
└─└─

Loi normale

Normal Distribution D016011 - N06.850.520.830.994.500
└─└─

Loi de Poisson

Poisson Distribution D016012 - N06.850.520.830.994.750
└─└─

Chaines de Markov

Markov Chains D008390 - N06.850.520.830.996.500
└─└─

Survie sans rechute

Disease-Free Survival D018572 - N06.850.520.830.998.300
└─└─

Estimation de Kaplan-Meier

Kaplan-Meier Estimate D053208 - N06.850.520.830.998.650
└─└─

Survie sans progression

Progression-Free Survival D000077982 - N06.850.520.830.998.825
└─└─

Modèles des risques proportionnels

Proportional Hazards Models D016016 - N06.850.520.830.998.912
└─└─└─

Analyse canonique des corrélations

Canonical Correlation Analysis D000089342 - N06.850.520.830.150.500.250
└─└─└─

Modèles économétriques

Models, Econometric D017059 - N06.850.520.830.500.600.500
└─└─└─

Facteurs de protection

Protective Factors D065840 - N06.850.520.830.600.800.582
└─└─└─

Appréciation des risques

Risk Assessment D018570 - N06.850.520.830.600.800.715
└─└─└─

Facteurs de risque

Risk Factors D012307 - N06.850.520.830.600.800.725
└─└─└─

Secteur de recensement

Census Tract D000091368 - N06.850.520.830.933.249.500
└─└─└─

Agrégat spatio-temporel

Space-Time Clustering D013029 - N06.850.520.830.933.500.500
└─└─└─└─

Analyse des modes de défaillance et de leurs effets en soins de santé

Healthcare Failure Mode and Effect Analysis D000067495 - N06.850.520.830.600.800.715.500
└─└─└─└─

Facteurs de risque de maladie cardiaque

Heart Disease Risk Factors D000082742 - N06.850.520.830.600.800.725.500
└─└─└─└─└─

Facteurs de risque cardiométabolique

Cardiometabolic Risk Factors D000083202 - N06.850.520.830.600.800.725.500.500
└─└─└─└─└─

Paradoxe de l'obésité

Obesity Paradox D000094408 - N06.850.520.830.600.800.725.500.750

Sources (10000 au total)

Diffusion-tensor MR imaging of optic pathway in idiopathic intracranial hypertension.

To investigate the role of diffusion tensor imaging (DTI) of optic pathway in patients with idiopathic intracranial hypertension (IIH).... This study included 41 IIH patients and 22 controls. They underwent DTI of the optic nerve (ON) and optic radiation (OR). Their fractional anostrophy (FA) and mean diffusivity (MD) were calculated by ... The optic nerve mean FA and MD of patients for reviewer-1 were 0.21 ± 0.047 and 2.189 ± 0.52 ×10... Our findings suggest that, IIH is associated mainly with pre-chiasmatic or ON involvement rather than post-chiasmatic parenchymal or OR involvement. DTI MD and FA parameters of the ON may be a reliabl...

Analysis of changes in brain morphological structure of taekwondo athletes by diffusion tensor imaging.

Taekwondo, which is the most preferred sport among the martial arts, is known to improve individuals physically, spiritually and mentally. The aim of this study is to reveal the effect of teakwondo sp... Total brain volume and volumes of white matter, gray matter, frontal lobe, precentral gyrus, corticospinal tract, basal nuclei, postcentral gyrus, hippocampus and amigdala and the ratio of these volum... An increase in total brain volume, gray matter, frontal lobe and precentral gyrus volume in athletes was associated with taekwondo training. When the ratio of brain parts to total brain volume was exa... The increase in the volume of gray matter, frontal lobe, postcentral gyrus and corticospinal tract together with the brain volume shows that taekwondo exercise contributes to physical, spiritual and m...

Probabilistic tractography of the extracranial branches of the trigeminal nerve using diffusion tensor imaging.

The peripheral course of the trigeminal nerves is complex and spans multiple bony foramen and tissue compartments throughout the face. Diffusion tensor imaging of these nerves is difficult due to the ... We prospectively acquired imaging data from six healthy adult participants with a 3.0-Tesla system, including T2-weighted short tau inversion recovery with variable flip angle (T2-STIR-SPACE) and read... All nerves in all subjects were tracked beyond the trigeminal ganglion. Tracts in the inferior alveolar and ophthalmic nerve exhibited the strongest signal and most consistently reached the most dista... This work demonstrates a method for consistently identifying and tracking the major nerve branches of the trigeminal nerve with diffusion tensor imaging....

Chronic Cocaine Use and White Matter Coherence: A Diffusion Tensor Imaging Study.

Chronic substance use and its effects on brain function and structure has long been of interest to clinicians and researchers. Prior cross-sectional comparisons of diffusion tensor imaging (DTI) metri... A total of 46 participants (21 healthy controls, 25 chronic cocaine users) were recruited from the Richmond, Virginia metropolitan area. Information regarding past and current substance use was collec... Consistent with previous DTI studies, significant differences were found between fractional anisotropy (FA) and axial diffusivity (AD) CocUD and controls, with CocUD showing lower FA and AD in the rig... These data align with previously reported declines in white matter coherence in chronic cocaine users. However, it is less clear whether comorbid alcohol consumption results in an additive deleterious...

Validation of Presurgical Simulation of White Matter Damage Using Diffusion Tensor Imaging.

The understanding of white matter (WM) was revolutionized by the emergence of tractography based on diffusion tensor imaging (DTI). Currently, DTI simulations are implemented in preoperative planning ... WM surgical damage was preoperatively simulated by creating a 3-dimensional volume representing the endoscope or the surgical trajectory. This volume was used as an additional region of interest in th... The preoperative and postoperative DTI studies of 30 patients undergoing various neurosurgical approaches were processed. The correlation between simulated injuries and postoperative studies was high ... Presurgical simulation of WM fascicles based on DTI is consistent with postoperative DTI studies. These findings require further validation by neurophysiological and clinical correlation....

Simultaneous Acquisition of Diffusion Tensor and Dynamic Diffusion MRI.

Dynamic diffusion magnetic resonance imaging (ddMRI) metrics can assess transient microstructural alterations in tissue diffusivity but requires additional scan time hindering its clinical application... To determine whether a diffusion gradient table can simultaneously acquire data to estimate dynamic and diffusion tensor imaging (DTI) metrics.... Prospective.... Seven healthy subjects, 39 epilepsy patients (15 female, 31 male, age ± 15).... Two-dimensional diffusion MRI (b = 1000 s/mm... Fifteen direction DTI was repeated twice for within-session test-retest measurements in healthy subjects. Bland-Altman analysis computed bias and limits of agreement for DTI metrics using test-retest ... Bland-Altman and ICC analysis using a P-value of 0.05 for statistical significance.... Correlations of mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were strong and significant in gray (ICC > 0.95) and white matter (ICC > 0.95) between standard vs. nested cu... A nested cubes diffusion gradient table produces tensor-based and dynamic diffusion measurements in a single acquisition.... 2 TECHNICAL EFFICACY: Stage 1....