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Record W2326917121 · doi:10.5430/jha.v5n3p76

Physician awareness of patient cardiac telemetry monitoring

2016· article· en· W2326917121 on OpenAlexvenueno aff
Poonam Sharma, Alan Tesson, Adam Wachter, Samantha Thomas, Jonathan G. Bae

Bibliographic record

VenueJournal of Hospital Administration · 2016
Typearticle
Languageen
FieldMedicine
TopicHealthcare Technology and Patient Monitoring
Canadian institutionsnot available
Fundersnot available
KeywordsTelemetryMedicineMedical emergencyGuidelineALARMCardiac monitoringBiotelemetryEmergency medicineComputer scienceCardiologyEngineeringTelecommunicationsPathology

Abstract

fetched live from OpenAlex

Overuse of cardiac telemetry monitoring in the inpatient setting is widespread, contributes to alarm fatigue, and is costly for health systems. We sought to quantify the rates of provider unawareness of ongoing telemetry use and to quantify the rate of appropriate monitoring compared to American Heart Association (AHA) guidelines using a survey design. Inpatient medical providers were questioned about the presence of telemetry for each of their patients. In the 870 inquiries, 47% of patients were receiving telemetry. Providers’ awareness of whether their patient was receiving telemetry was inaccurate in 26% of assessments. A guideline-appropriate indication for telemetry use was provided in only 58% of assessments. Providers are often unaware of ongoing cardiac telemetry use in their patients, and may continue use despite the lack of a guideline-appropriate indication.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.708
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.310
Teacher spread0.293 · 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

Citations5
Published2016
Admission routes1
Has abstractyes

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