Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".