Integration of monitoring technology for heart failure
Bibliographic record
Abstract
PURPOSE OF REVIEW: Death and hospitalization for acutely decompensated heart failure are predominantly due to volume overload, have increased in the past 10 years, carry poor outcomes, and are costly to the healthcare system. Clinical monitoring with daily weight and symptoms is relatively insensitive for early detection of volume overload. This lack of diagnostic accuracy has led to the development of technologies as an aid for early detection of clinical decompensation. RECENT FINDINGS: Multidisciplinary heart failure clinics are currently the standard of care; however, recent large randomized studies have failed to show benefits in comparison to usual care when follow-up is frequent. Technologies, such as intrathoracic impedance monitoring incorporated into implantable cardioverter defibrillators (ICDs) and cardiac resynchronization therapy (CRT) devices, have shown variable sensitivity and low positive predictive value in predicting heart failure hospitalizations. As such, they have yet to consistently show efficacy in reducing hospitalizations for heart failure. Implantable hemodynamic monitoring devices have shown promise in reducing hospitalization rates for acutely decompensated heart failure, over and above heart failure clinic care. However, these studies were performed on younger populations with relatively few noncardiac comorbidities and have not been studied in the typical elderly heart failure patient populations. SUMMARY: The ability to acquire hemodynamic data with the help of implanted devices can provide early warning of heart failure decompensation and thus may aid in preventing hospitalizations for heart failure. Further studies will clarify patient populations most likely to benefit from this intervention.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".