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Record W2746719527 · doi:10.1016/j.jvs.2017.05.123

Association between operator specialty and outcomes after carotid artery revascularization

2017· article· en· W2746719527 on OpenAlexafffundabout
Mohamad A. Hussain, Muhammad Mamdani, Jack V. Tu, Gustavo Saposnik, Konrad Salata, Deepak L. Bhatt, Subodh Verma, Mohammed Al‐Omran

Bibliographic record

VenueJournal of Vascular Surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreInstitute for Clinical Evaluative SciencesUniversity of TorontoSt. Michael's Hospital
FundersEisaiUniversity of TorontoOntario Ministry of Health and Long-Term CareMedicines CompanyAmarin CorporationIronwood Pharmaceuticals, IncorporatedInstitute for Clinical Evaluative SciencesSanofiAmgenPfizerAstraZenecaEli Lilly and CompanyBristol-Myers Squibb
KeywordsMedicineCarotid endarterectomyCarotid stentingEndarterectomyStroke (engine)Odds ratioSpecialtyPopulationConfidence intervalSurgeryRevascularizationInternal medicineCardiologyRadiologyStenosisMyocardial infarction

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the association between operator specialty and 30-day outcomes among patients undergoing carotid endarterectomy and carotid artery stenting. METHODS: We conducted a population-based, observational cohort study of all individuals who underwent carotid endarterectomy or stenting in Ontario, Canada (population, 13.6 million) between April 1, 2002, and March 1, 2015, using administrative claims databases. We stratified endarterectomy and stenting patients according to operator specialty, and followed them for 30 days after the procedure. For carotid endarterectomy, we compared outcomes between vascular surgeons and nonvascular surgeons. For carotid artery stenting, we compared outcomes between radiologists and neurosurgeons. We built multilevel multivariable logistic regression models adjusted for patient demographics, comorbidities, carotid artery symptom status, and annual institutional and operator volume to examine rates of 30-day stroke or death. RESULTS: A total of 16,544 patients were studied (n = 14,301 endarterectomy and n = 2243 stenting). Vascular surgeons performed the majority (55.7%) of carotid endarterectomy procedures, followed by neurosurgeons (21.0%), general surgeons (15.3%), and cardiac surgeons (7.9%). Radiologists (82.5%) and neurosurgeons (17.5%) performed carotid artery stenting. In the endarterectomy group, the risk of stroke or death was higher among patients treated by nonvascular surgeons (4.0%) compared with vascular surgeons (2.9%; adjusted odds ratio [OR], 1.32; 95% confidence interval [CI], 1.08-1.62; P = .008). This difference was driven by a higher rate of stroke among nonvascular surgery-treated patients (3.6%) compared with vascular surgery-treated patients (2.5%; adjusted OR, 1.38; 95% CI, 1.11-1.71). The risk of death was similar between the two groups. With respect to specific nonvascular surgery specialties, the rate of 30-day stroke or death was higher in endarterectomy patients treated by neurosurgeons (4.1%; adjusted OR, 1.27; 95% CI, 1.00-1.61) and cardiac surgeons (4.4%; adjusted OR, 1.54; 95% CI, 1.04-2.30) compared with vascular surgeons (2.9%). Patients who underwent carotid artery stenting by radiologists vs neurosurgeons experienced 30-day stroke or death at similar rates (8.0% vs 7.9%, respectively; adjusted OR, 1.07; 95% CI, 0.66-1.74; P = .79). CONCLUSIONS: The risk for periprocedural stroke or death was significantly higher among carotid endarterectomy patients treated by nonvascular surgeons (neurosurgeons and cardiac surgeons) compared with vascular surgeons. Operator specialty did not seem to have a significant effect on periprocedural outcomes among patients who underwent carotid artery stenting. These results can have implications for physician referral practices and local policies.

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)0.002
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 score0.002
Threshold uncertainty score0.615

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.013
GPT teacher head0.254
Teacher spread0.241 · 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

Citations24
Published2017
Admission routes3
Has abstractyes

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