Clinical approaches to the diagnosis of acute heart failure
Bibliographic record
Abstract
PURPOSE OF REVIEW: Predicting which patients with congestive heart failure will decompensate is often difficult, and it is often difficult to distinguish congestive heart failure from other causes of acute dyspnea. This review will focus on some of the newer tools used to diagnose acute congestive heart failure in addition to reviewing the utility of more traditional tools. RECENT FINDINGS: The integration of pertinent positives and negatives on a routine history, key physical findings on examination and routine noninvasive imaging offers high positive and negative predictive power for the diagnosis of acute heart failure. Measurement of B-type natriuretic peptide and N-terminal proB-type natriuretic peptide offers additional and incremental diagnostic information. Measurement of intrathoracic impedance is a novel and potentially useful tool to track absolute changes in cardiac function and total lung fluid content, and may be useful for the outpatient titration of medical therapy to minimize acute congestive heart failure decompensation. SUMMARY: Consistent accurate diagnosis of decompensated congestive heart failure is possible using no more than a complete history and physical examination along with routine imaging techniques. The ability to diagnose acute congestive heart failure however, is improved by using serum B-type natriuretic peptide and intrathoracic impedance, both of which offer additive and complementary diagnostic information.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".