Correlation Between Centrally Versus Peripherally Transduced Venous Pressure in Prone Patients Undergoing Posterior Spine Surgery
Bibliographic record
Abstract
STUDY DESIGN: Prospective clinical observational study. OBJECTIVE: To evaluate the correlation and agreement between peripherally and centrally transduced venous pressures in prone spine surgery patients. SUMMARY OF BACKGROUND DATA: In view of a variety of potential complications associated with the placement of central venous lines for the purpose of central venous pressure (CVP) monitoring, a number of authors have suggested that the use of peripherally transduced pressures (PVP) instead may yield similar results. Data confirming the validity of this technique for the purpose of intravascular fluid volume monitoring in prone patients undergoing spine surgery remain scarce. METHODS: After protocol approval by the internal review board, we enrolled 40 patients who underwent spine surgery in the prone position. CVP and PVP were recorded simultaneously. The data pairs were analyzed for correlation. Bland and Altman plots were created to evaluate the degree of agreement between the 2 modes of venous pressure monitoring. RESULTS: A total of 1275 data pairs were collected. The mean PVP was 17.55 mm Hg +/- 4.93 mm Hg and the mean CVP 15.52 mm Hg +/- 4.77 mm Hg (P < 0.001), thus yielding a mean difference of 2.04 mm Hg +/- 1.39 mm Hg. PVP and CVP correlated well over a wide range of pressures (r = 0.949, r = 0.920 [P < 0.001]). A high level of agreement was found between both methods of venous pressure measurement. CONCLUSION: CVP and PVP correlate well under conditions associated with prone spine surgery. With a high level of agreement found in this study, PVP may represent an attractive alternative to CVP monitoring to assess fluid volume trends intraoperatively.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".