Non-Invasive Bedside Assessment of Central Venous Pressure: Scanning into the Future
Bibliographic record
Abstract
BACKGROUND: Noninvasive evaluation of central venous pressure (CVP) can be achieved by assessing the Jugular Venous Pressure (JVP), Peripheral Venous Collapse (PVC), and ultrasound visualization of the inferior vena cava. The relative accuracy of these techniques compared to one another and their application by trainees of varying experience remains uncertain. We compare the application and utility of the JVP, PVC, and handheld Mini Echo amongst trainees of varying experience including a medical student, internal medicine resident, and cardiology fellow. We also introduce and validate a new physical exam technique to assess central venous pressures, the Anthem sign. METHODS: Patients presenting for their regularly scheduled echocardiograms at the hospital echo department had clinical evaluations of their CVP using these non-invasive bedside techniques. The examiners were blinded to the echo results, each other's assessments, and patient history; their CVP estimates were compared to the gold standard level 3 echo-cardiographer's estimates at the completion of the study. RESULTS: 325 patients combined were examined (mean age 65, s.d. 16 years). When compared to the gold standard of central venous pressure by a level 3 echocardiographer, the JVP was the most sensitive at 86%, improving with clinical experience (p<0.01). The classic PVC technique and Anthem sign had better specificity compared to the JVP. Mini Echo estimates were comparable to physical exam assessments. CONCLUSIONS: JVP evaluation is the most sensitive physical examination technique in CVP assessments. The PVC techniques along with the newly described Anthem sign may be of value for the early learner who still has not mastered the art of JVP assessment and in obese patients in whom JVP evaluation is problematic. Mini Echo estimates of CVPs are comparable to physical examination by trained clinicians and require less instruction. The use of Mini Echo in medical training should be further evaluated and encouraged.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".