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Record W2770878121 · doi:10.15273/dmj.vol44no1.7575

The use of point of care ultrasonography in the perioperative period: A case of Takotsubo cardiomyopathy

2017· article· en· W2770878121 on OpenAlexaffvenue
Nicholas Humphreys, WILLIAM N. GALLACHER

Bibliographic record

VenueDalhousie Medical Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicTakotsubo Cardiomyopathy and Associated Phenomena
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicinePerioperativeCardiomyopathyPulseless electrical activityCardiologyEjection fractionInternal medicineIschemic cardiomyopathyResuscitationCardiopulmonary resuscitationAnesthesiaHeart failure

Abstract

fetched live from OpenAlex

Takotsubo cardiomyopathy is a stress induced, transient cardiomyopathy that commonly presents with regional left ventricular akinesis or hypokinesis. The perioperative incidence of Takotsubo is estimated at 1:6700 cases and as such, should be familiar to practicing anesthesiologists. Point of care ultrasonography is a valuable tool for the evaluation of cardiac function and can be utilized in the perioperative setting by anesthesia staff. With some basic training in ultrasonography, one can quickly assess volume status and cardiac function, among other things. We present the case of a 73-year-old male undergoing an elective rotator cuff repair with an interscalene block under general anesthesia. Immediately following induction, the patient had a pulseless electrical activity arrest. Following immediate resuscitation, point of care ultrasonography was used to delineate the cause of the arrest and guide immediate management. Features consistent with Takotsubo cardiomyopathy – including regional left ventricular wall motion abnormalities with associated low ejection fraction – were identified on early ultrasound. The patient made a successful recovery and repeat cardiac imaging showed resolution of the wall motion abnormalities.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.026
GPT teacher head0.290
Teacher spread0.264 · 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

Citations0
Published2017
Admission routes2
Has abstractyes

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