Validation and Experience With a Simple Continuous Intra-abdominal Pressure Measurement Technique in a Multidisciplinary Medical/Surgical Critical Care Unit
Bibliographic record
Abstract
BACKGROUND: Raised intra-abdominal pressure (IAP) or intra-abdominal hypertension (IAH) may induce many adverse effects including the abdominal compartment syndrome. We evaluated a new technique for continuous monitoring of intra-abdominal pressure (CIAP) using a standard three-way bladder catheter in a diverse group of intensive care unit patients. METHODS: CIAP measured using a standard three-way bladder catheter was compared with five standard intermittent IAP (IIAP) measurements in 79 patients. RESULTS: Mean (standard deviation) CIAP was identical (15.4 mm Hg [5.8]) for CIAP and IIAP one minute after saline injection. Mean differences between methods were less than 1 mm Hg, and similar whether IIAP was measured at 1 minute, 2 minutes, 3 minutes, 4 minutes, or 5 minutes. Bland-Altman analysis comparing CIAP and IIAP (1 minute) revealed a mean difference (95% confidence interval) of -0.06 mm Hg (-0.51, 0.39). Limits of agreement were -4.12 mm Hg to 4.00 mm Hg. Considering gradations of IAH defined by the World Society of the Abdominal Compartment Syndrome, CIAP was sensitive for detecting slightly elevated IAP (>11 mm Hg) but is less sensitive for distinguishing between higher grades of IAH (e.g., pressures >20 mm Hg or 25 mm Hg). Limits of agreement were best for patients with IAP less than 20 mm Hg, surgical or traumatic diagnoses and for patients with BMI less than 26. CONCLUSIONS: Overall, CIAP is an accurate and simple means of measuring IAP when compared with the current standardized method. Elevated CIAP measurements should be confirmed with IIAP measurements if accurate grading is required until further validation and experience is obtained.
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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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".