Notice bibliographique
Résumé
Congestive Heart Failure (CHF) is a common complex clinical syndrome that underlines the inability of the heart to perform its circulatory function with the desired efficiency due to structural and/or functional alterations. There is paucity of good and reliable data in India and many developing countries on heart failure. The management of heart failurehas evolved over the years with the advent of new drugs and devices.But there is a need to uneartha true and meaningful nationaldata on the risk factors,available treatment options,and challenges in management that could be addressed to take advantage of the recent advances. CHF is a disease of the “elderly,” frequently occurs in the setting of normal ejection fraction, and has a poor prognosis, regardless of the level of systolic function. The highest prevalence of CHF is reported among Indigenous Australian population (5.3%), Germany (4%) and Canada 3.6%, Turkey 2.9%, and USA 2.6% as compared to only 0.3% in Indian population. Overall, more than 5 persons aged 60 to 69 and 10 persons per 1,000 population after 65 years of age suffer from CHF. The incidence of CHF is equally frequent in men and women globally, but it is more amongelderly women in India comparedto elderly men. The burden of heart failure is increasing at an alarming rate worldwide as well as in India. CHF not only increases the risk of mortality, morbidity and worsens the patient’s quality of life, but also puts a huge burden on the overall healthcare system. We need to acknowledge the fact that diagnostic and therapeutic methods available are also underused in the community. This review article is the result of witnessing the heart failure in 4 individuals in January 2023. Their symptomsand signs included Shortness of breath with routine activity like walking or household chores, fatigue, and weakness, Pedal oedema, rapid or irregular heartbeat, fluctuating Blood Pressure and Blood sugar levels, reduced ability to exercise and vomiting and aspirational pneumonia. The exponential rise in the incidence of uncontrolled hypertension and DM over the last couple of years has shaped the trajectory of HF development seen today. The key risk factors and causes of HF in our cases included hypertension (HT), diabetes mellitus (DM); chronic kidney disease (CKD). With the bestpossible management practices in cities likeBengaluru and Mysuru in Karnataka we could save only two of the 4 cases,both first-time hospitalized patients. Materials & Methods: The third week of January2023 (17-25 January), the author had a misfortune being a witnessfor 3 women and one man between 64-85 years of age’s hospitalized for CHF with an outcome of 50% of them succumbing to CHF. This manuscript is a review of available information on the websites of World Heart Federation 2020, WHO, Global burden of disease 2019 report, ICC - National Heart Failure Registry, Reports of the Best Charities that fight Heart Diseases in 2023 including American Heart Association, The Children’s Heart Foundation, British Heart Foundation, Mended Hearts, Women Heart, Needy heart Foundation Bangalore and published papers in Indiaas evidences.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».