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Temporal Trends in Percutaneous Coronary Intervention Appropriateness

2015· article· en· W1480079961 on OpenAlexaff
Steven M. Bradley, Camden Bohn, David J. Malenka, Michelle M. Graham, Chris L. Bryson, James M. McCabe, Jeptha P. Curtis, Anne Lambert‐Kerzner, Charles Maynard

Bibliographic record

VenueCirculation · 2015
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsUniversity of AlbertaCapital District Health Authority
Fundersnot available
KeywordsMedicineAppropriateness criteriaPercutaneous coronary interventionCardiologyAppropriate Use CriteriaIntensive care medicineInternal medicineRadiologyMyocardial infarction

Abstract

fetched live from OpenAlex

BACKGROUND: It is unknown whether the appropriate use of percutaneous coronary intervention (PCI) has improved over time and whether trends in PCI appropriateness have been accompanied by changes in the use of PCI. METHODS AND RESULTS: We applied appropriate use criteria to determine the appropriateness of all 51 872 PCI performed in Washington State from 2010 through 2013. We evaluated the number of PCIs performed from 2006 through 2013 to provide a comparator period that preceded statewide appropriateness assessment beginning in 2010. Between 2010 and 2013, the overall number of PCI decreased by 6.8% (13 267 PCIs in 2010 to 12 193 in 2013) with a 43% decline in the number of PCIs for elective indications (3818 PCIs in 2010 to 2193 PCIs in 2013). The decline in the use of elective PCI was significantly larger after the onset of statewide PCI appropriateness assessment in 2010 (P=0.03). The proportion of elective PCIs classified as appropriate increased from 26% in 2010 to 38% in 2013, whereas the proportion of inappropriate PCIs decreased from 16% to 13% (P<0.001 for trends). Significant improvements in the proportion of inappropriate PCI were limited to the tertile of hospitals with the largest decline in PCIs classified as inappropriate (25% in 2010 to 12% in 2013; P=0.03). CONCLUSIONS: In Washington State, the use of PCI for elective indications has decreased over time with concurrent improvements in PCI appropriateness. However, improvements in PCI appropriateness were limited to a minority of hospitals. Understanding processes at these high-performing hospitals may inform efforts to improve PCI appropriateness.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.302

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 head0.071
GPT teacher head0.350
Teacher spread0.279 · 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

Citations68
Published2015
Admission routes1
Has abstractyes

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