MétaCan
Menu
Back to cohort
Record W4401078531 · doi:10.1136/bmj-2023-077499

Decreasing unnecessary use of continuous cardiac monitoring (telemetry) in hospitalised patients

2024· article· en· W4401078531 on OpenAlexaff
William K. Silverstein, Irene Y Chang, Shiva Sreenivasan, Sanket S. Dhruva

Bibliographic record

VenueBMJ · 2024
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsWomen in Science and Engineering Newfoundland and LabradorUniversity of Toronto
Fundersnot available
KeywordsTelemetryMedicineCardiac monitoringIntensive care medicineClinical PracticeMedical emergencyEmergency medicineCardiologyComputer science

Abstract

fetched live from OpenAlex

### What you need to know Since its development in 1949, in-hospital continuous electrocardiographic monitoring (hereinafter “telemetry”) has become increasingly important for clinical care of hospitalised patients.1 Telemetry is used for a variety of applications, including diagnosis and monitoring of arrhythmias, detection of myocardial ischaemia, and monitoring of ST segments and QT intervals. Specialist societies around the world have published practice standards to inform clinicians when telemetry should be used.234 Appropriate clinical indications are listed in box 1 and include patients with suspected or confirmed acute coronary syndromes, acute decompensated heart failure, high grade arrhythmias, post-cardiac arrest, severe electrolyte derangements, use of certain medications, and ingestion of pro-arrhythmic agents.234 Box 1 ### Indications for appropriate continuous electrocardiographic monitoring recommended by international specialist societies #### Agency for Clinical Innovation’s Clinical Practice Guide (Australia)2RETURN TO TEXT

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.057
Threshold uncertainty score0.442

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.343
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueBMJSame topicHealthcare Technology and Patient MonitoringFrench-language works237,207