Incidence of Intra-Abdominal Hypertension and Abdominal Compartment Syndrome: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To determine the contemporary prevalence of intra-abdominal hypertension (IAH) and abdominal compartment syndrome in critically ill patients. DATA SOURCES: Medline, Embase, and Central databases. STUDY SELECTION: Studies reporting on the prevalence of IAH in consecutively admitted critically ill patients using the World Society of Abdominal Compartment Syndrome (WSACS) consensus guidelines for intra-abdominal pressure (IAP) measurement. DATA EXTRACTION: Duplicate independent review and data abstraction. DATA SYNTHESIS: The search identified 2428 titles with 6 eligible studies (n = 1965). Reported prevalence ranged from 30% to 49%. Despite abiding by the WSACS guidelines for IAP measurement, studies varied in their definition of IAH, frequency and duration of IAP measurement, and reporting of outcomes. Three of 6 studies reported that IAH, especially at higher grades, was an independent predictor of mortality. CONCLUSIONS: Intra-abdominal hypertension is a common finding in critically ill patients and may be associated with increased mortality, especially at higher grades. Further prospective research is required to examine the effect of screening and treatment of IAH on patient outcomes.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".