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Record W2948865799 · doi:10.1080/13696998.2019.1627364

Cost of operating room time for endovascular transcatheter aortic valve replacement

2019· article· en· W2948865799 on OpenAlexaff
Brian J. Potter, Christin Thompson

Bibliographic record

VenueJournal of Medical Economics · 2019
Typearticle
Languageen
FieldMedicine
TopicCardiac Valve Diseases and Treatments
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineValve replacementComorbidityCohortCharlson comorbidity indexEmergency medicineVariable costSurgeryCardiologyInternal medicineStenosis

Abstract

fetched live from OpenAlex

Background: Procedural efficiencies can contribute to cost reductions in transcatheter aortic valve replacement procedures (TAVR). The objective of this study is to determine operating room (OR) variable cost per minute in endovascular TAVR procedures, in a real-world hospital setting.Methods: Using Premier data from January 2015–June 2016 for patients undergoing a primary endovascular TAVR (primary ICD-9 code of 35.05, ICD-10 code of 02RF37Z, 02RF38Z, 02RF3JZ, or 02RF3KZ) procedure, the OR cost per minute was calculated for each patient by dividing the total hospital OR variable cost by the OR time (minutes).Results: Of the 4,573 patients in the cohort, the average age was 80 years, 77% were admitted electively, and the vast majority were discharged home with (30%) or without (45%) home care. Median OR time for endovascular TAVR procedures was 180 min. The trimmed mean OR cost per minute was $43.59 (SD = $28.68). When stratified by Elixhauser Risk score and Charlson comorbidity index, OR cost per minute increased with higher risk and comorbidity (p < 0.0001 and p < 0.041, respectively).Conclusions: This contemporary estimate of the real-world variable OR cost per minute provides researchers with a critical parameter to refine economic models of TAVR and aid clinical program directors in resource planning according to a priori risk and comorbidity.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.293
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
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)0.0010.000

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 head0.016
GPT teacher head0.316
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

Citations10
Published2019
Admission routes1
Has abstractyes

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