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Enregistrement W4249174129 · doi:10.37575/b/med/0038

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2021· article· en· W4249174129 sur OpenAlexaboutno aff
Emad Al Khoufi

Notice bibliographique

RevueScientific Journal of King Faisal University Basic and Applied Sciences · 2021
Typearticle
Langueen
DomaineEnvironmental Science
ThématiqueBusiness and Economic Development
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineDiabetes mellitusMyocardial infarctionBlood pressureInternal medicineCoronary artery diseaseCardiologyEpidemiologyIncidence (geometry)DiseaseEndocrinology

Résumé

récupéré en direct d'OpenAlex

Few epidemiological studies have discussed the gender-specific prevalence of ischemic heart disease (IHD). We aimed to investigate the gender-specific prevalence of IHD among Saudi patients visiting the emergency department and if it is affected by diabetes mellitus and/or hypertension. Three hundred patients were recruited from Prince Sultan Cardiac Center in Al Ahsa, KSA. Hypertension was identified as systolic pressure equal to or more than 140 mmHg and/or diastolic pressure equal to or more than 90 mmHg or by the patient currently being on antihypertensive medication, and coronary artery disease (CAD) was diagnosed by electrocardiogram, cardiac markers, cardiac exercise testing or coronary angiography. Hypertension was found in 80% of males and 72% of females. A significantly higher rate of diabetes was noted in females (62%) compared to males (48%) (p<0.012). Co-existing diabetes and hypertension was found in 70% of females as compared to 38% of males. The occurrence of IHD in males was significantly higher than that in females (p<0.001). However, the incidence of myocardial infarction was greater in females (52%) compared to males (38%) (p<0.035). Co-existing hypertension and diabetes may affect the gender prevalence of myocardial infarction among emergency department patients, with more infarctions being noted among females. This finding helps to guide the treatment strategy for both genders.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,985
Score d'incertitude au seuil0,051

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,004
Études des sciences et des technologies0,0020,002
Communication savante0,0020,005
Science ouverte0,0000,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0150,002

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,107
Tête enseignante GPT0,346
Écart entre enseignants0,239 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations21
Publié2021
Routes d'admission1
Résumé présentoui

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Même revueScientific Journal of King Faisal University Basic and Applied SciencesMême sujetBusiness and Economic DevelopmentTravaux en français237 207