My patient has no blood pressure: is their heart working?
Bibliographic record
Abstract
How can point-of-care (PoC) ultrasound be used to help the clinician identify cardiogenic and certain non-cardiogenic causes of undifferentiated, non-traumatic hypotension? Hypotension is a common emergency presentation in the emergency department and medical admissions unit. Due to the physiological complexities of this state, the aetiology is often unclear to clinicians at initial presentation. This has clear implications for treatment and outcomes of the patient. Clinical indicators such as vital signs and physical examination are often unreliable in distinguishing causes of hypotension, identifying the correct aetiology in only 25–50% of cases. PoC ultrasound (PoCUS) of the heart can help differentiate between cardiogenic causes such as left ventricular dysfunction, and obstructive causes such as cardiac tamponade, and can point towards other causes such as sepsis and hypovolaemia. Limited cardiac echocardiography or ‘echo in life support’ can be performed as a part of goal-directed protocols for undifferentiated, non-traumatic hypotension. Use of such protocols early can significantly improve the diagnostic accuracy to 80%, thereby reducing the time to diagnosis and the time to initiating appropriate therapies. In this article, we review our approach to PoCUS of the heart in the setting of hypotension.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".