Continuous Electrocardiographic Monitoring and Cardiac Arrest Outcomes in 8,932 Telemetry Ward Patients
Bibliographic record
Abstract
OBJECTIVE: To estimate the benefit of routine electrocardiographic (ECG) telemetry monitoring on in-hospital cardiac arrest survival. METHODS: In a tertiary care hospital, all telemetry ward admissions and cardiac arrests occurring over a five-year period were reviewed. Ward location and survival to discharge were determined for all patients outside of critical care areas. RESULTS: During the study period, 8,932 patients were admitted to the telemetry ward, and 20 suffered cardiac arrest (0.2%; 95% CI = 0.1 to 0.3). Telemetry monitors signaled the onset of cardiac arrest in only 56% (95% CI = 30 to 80) of monitored arrests. Three patients survived to discharge, and in two of these three patients the arrest onset was signaled by the monitor. This yields a monitor-signaled survival rate among telemetry ward patients of 0.02% (95% CI = 0 to 0.05). All survivors suffered significant arrhythmias prior to their cardiac arrests. CONCLUSIONS: Cardiac arrest is an uncommon event among telemetry ward patients, and monitor-signaled survivors are extremely rare. Routine telemetry offers little cardiac arrest survival benefit to most monitored patients, and a more selective policy for telemetry use might safely avoid ECG monitoring for many patients.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".