A Case of Multifaceted Assessment in an Elderly Patient With Acute Decompensated Heart Failure
Bibliographic record
Abstract
Heart failure (HF) is a common health problem worldwide, including in Japan. Unfortunately, patient outcomes remain poor, with a 5-year survival rate of approximately 50%. Therefore, we need to assess the precise conditions, including cardiac function, in patients with HF, particularly in the elderly. We performed a multifaceted assessment in an elderly patient with HF on admission and at discharge using eight different evaluations (the mean life expectancy using the Seattle Heart Failure Model (SHFM), the severity of dementia, nutrition, medication adherence, biomarker (the level of brain natriuretic peptide in blood), sociality, performance and comorbidity). Each parameter was scored on a 5-point scale (excellent = 5 points; good = 4 points; fair (average) = 3 points; poor = 2 points; failure = 1 point; maximum total points of 40) ( F ukuoka U niversity Heart F ailure S coring System, FUFS). An 86-year-old male patient who complained of dyspnea and lower-leg edema was admitted to our university hospital due to acute decompensated HF. After treatment, his symptoms improved, as did his cardiothoracic ratio, plural effusion and pulmonary congestion, and he exhibited compensated HF. His total score improved from 28 to 32 points, and his mean life expectancy using SHFM increased from 4.9 to 5.4 years. We evaluated the precise conditions using a multifaceted assessment strategy in an elderly patient with HF. The strategy was useful for evaluate the patient’s condition in this case. Cardiol Res. 2017;8(6):339-343 doi: https://doi.org/10.14740/cr640w
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".