The use of point of care ultrasonography in the perioperative period: A case of Takotsubo cardiomyopathy
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".