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Record W4391749980 · doi:10.1177/17504589231223011

An audit of postoperative haemodynamic stability after intraoperative labetalol administration in non-cardiac surgery patients

2024· article· en· W4391749980 on OpenAlexaff
Benjamin B Cairns, Megan V. McNeil, Andrew D. Milne

Bibliographic record

VenueJournal of Perioperative Practice · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsLabetalolMedicineAnesthesiaCardiac surgeryHemodynamicsSurgeryBlood pressureInternal medicine

Abstract

fetched live from OpenAlex

Anaesthesiologists commonly use intravenous labetalol to adjust patient haemodynamics during surgical procedures. Cases of profound hypotension after continuous labetalol infusions have been reported; however, there is limited evidence regarding the safety of intraoperative labetalol boluses. This audit examined the frequency of postoperative hypotension and bradycardia in 292 adult non-cardiac surgery patients treated with intraoperative labetalol boluses. Blood pressure and heart rate data were collected from the post-anaesthesia care unit and on the floor units for 24 hours after surgery. The median total intraoperative labetalol dose was 10mg. A total of 30/292 patients had all-cause postoperative hypotension within 24 hours of surgery, 26 of which had other medical or surgical precipitants. Fifteen patients developed bradycardia. There were no deaths or intensive care unit admissions attributed to labetalol. This audit demonstrates a low risk of all-cause postoperative hypotension (10%) and bradycardia (5%) after the use of small IV doses of intraoperative labetalol.

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 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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.311
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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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