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Record W2328124010 · doi:10.1136/ebmed-2016-110395

Preoperative treatment with β-blockers is associated with elevated postoperative mortality and cardiac morbidity in patients with uncomplicated hypertension undergoing non-cardiac surgery

2016· letter· en· W2328124010 on OpenAlexaff
Duminda N. Wijeysundera

Bibliographic record

VenueEvidence-Based Medicine · 2016
Typeletter
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsSt. Michael's Hospital
Fundersnot available
KeywordsMedicineMyocardial infarctionPerioperativeStroke (engine)Heart failureInternal medicineSurgery

Abstract

fetched live from OpenAlex

Commentary on: Jørgensen ME, Hlatky MA, Køber L, et al. β-Blocker-associated risks in patients with uncomplicated hypertension undergoing noncardiac surgery. JAMA Intern Med 2015;175:1923–31.[OpenUrl][1][PubMed][2] The role of β-blockers in preventing cardiovascular complications of non-cardiac surgery is controversial. Early enthusiasm was dampened by accumulating trial data and concerns about the scientific validity of several trials. When studies with uncertain validity are excluded, meta-analyses of randomised trials show that perioperative β-blockers (started within 1 day before surgery) prevent postoperative myocardial infarction (MI), but increase the risks of stroke and death.1 This Danish nationwide cohort study evaluated the association of long-term preoperative β-blocker treatment with mortality and cardiac morbidity after non-cardiac surgery in patients with uncomplicated hypertension. Population-based healthcare databases were used to conduct … [1]: {openurl}?query=rft.jtitle%253DJAMA%2BIntern%2BMed%26rft.volume%253D175%26rft.spage%253D1923%26rft_id%253Dinfo%253Apmid%252F26436291%26rft.genre%253Darticle%26rft_val_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Ajournal%26ctx_ver%253DZ39.88-2004%26url_ver%253DZ39.88-2004%26url_ctx_fmt%253Dinfo%253Aofi%252Ffmt%253Akev%253Amtx%253Actx [2]: /lookup/external-ref?access_num=26436291&link_type=MED&atom=%2Febmed%2F21%2F3%2F114.atom

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.027
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.030
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0070.005
Insufficient payload (model declined to judge)0.0200.006

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 head0.055
GPT teacher head0.274
Teacher spread0.219 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Not applicable
Domainnot available
GenreCommentary

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
Published2016
Admission routes1
Has abstractyes

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