Acute Respiratory Distress Syndrome and Shunt Detection With Bubble Studies: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Acute respiratory distress syndrome (ARDS) is a life-threatening respiratory injury with multiple physiological sequelae. Shunting of deoxygenated blood through intra- and extrapulmonary shunts may complicate ARDS management. Therefore, we conducted a systematic review to determine the prevalence of sonographically detected shunts, and their association with oxygenation and mortality in patients with ARDS. DATA SOURCES: Medical literature analysis and retrieval system online, Excerpta Medica dataBASE, Cochrane Library, and database of abstracts of reviews of effects databases on March 26, 2021. STUDY SELECTION: Articles relating to respiratory failure and sonographic shunt detection. DATA EXTRACTION: Articles were independently screened and extracted in duplicate. Data pertaining to study demographics and shunt detection were compiled for mortality and oxygenation outcomes. Risk of bias was appraised using the Joanna-Briggs Institute and the Newcastle-Ottawa Scale tools with evidence rating certainty using Grading of Recommendations Assessment, Development and Evaluation methodology. DATA SYNTHESIS: From 4,617 citations, 10 observational studies met eligibility criteria. Sonographic detection of right-to-left shunt was present in 21.8% of patients (range, 14.4–30.0%) among included studies using transthoracic, transesophageal, and transcranial bubble Doppler ultrasonographies. Shunt prevalence may be associated with increased mortality (risk ratio, 1.22; 95% CI, 1.01–1.49; p = 0.04, very low certainty evidence) with no difference in oxygenation as measured by Pa o 2 :F io 2 ratio (mean difference, –0.7; 95% CI, –18.6 to 17.2; p = 0.94, very low certainty). CONCLUSIONS: Intra- and extrapulmonary shunts are detected frequently in ARDS with ultrasound techniques. Shunts may increase mortality among patients with ARDS, but its association with oxygenation is uncertain.
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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 | 0.015 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".