Does in-Patient ECG Monitoring have an Impact on Medical Care in Chronic Heart Failure Patients?
Bibliographic record
Abstract
BACKGROUND: Heart failure patients' management in non-intensive care units might be improved by telemetry monitoring. However, telemetry adds the cost and evidence of this effectiveness is not available. AIM: To evaluate the utility of the ECG monitoring in chronic heart failure patients admitted to a non-intensive care unit. METHODS: A prospective analysis of the utility of telemetry in 711 patients admitted to a Heart Failure Unit from March 1996 to September 1997. RESULTS: One hundred and ninety-nine patients underwent telemetry; 108 telemetry findings were recorded, in 35% of NYHA class II, in 46% in NYHA class III-IV and 43% in unstable patients. Reasons for telemetry were: known arrhythmia (n=82), electrolytes disturbances (n=20), atrial fibrillation (n=12), symptoms (n=48), i.v. dobutamine (n=13), drugs control (n=16), devices control (n=8). Crossing reasons for telemetry and detected events we had, respectively, 63, 11, 2, 17, 5, 6, and 0 telemetry findings. Treatment was guided by telemetry results in only 33 cases (respectively in 18, 0, 4, 5, 5, 1, and 0 cases). Physicians perceived telemetry as unhelpful in 30% of cases; as helpful in 70%. The percentage of inutility, usefulness with and without related medical intervention were similar between stable and unstable patients (30, 18, 51% and 31, 15, 54%, respectively). CONCLUSION: In a heart failure unit ECG monitoring is mostly used in severe and unstable patients. However, medical decisions are rarely guided by the telemetry findings. The usefulness of telemetry might be underestimated because one of the uncounted results might be the avoidance of inappropriate 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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".