The dead space fraction as a prognostic death indicator in patients with ARDS: a systematic review and meta-analysis.
Bibliographic record
Abstract
OBJECTIVE: Acute respiratory distress syndrome (ARDS) is a systemic disease with high morbidity and mortality. Dead space fraction (Vd/Vt) represents the volume of air that does not participate in gas exchange and accurately depicts the pathophysiology of ARDS due to ventilation and perfusion mismatch. In this study, we aim to conduct a systematic review and meta-analysis regarding its usefulness for predicting mortality. MATERIALS AND METHODS: We performed a systematic literature search identifying comparative studies meeting the above criteria from four databases: MEDLINE, clinicaltrials.gov, CENTRAL, and Google Scholar. A statistical meta-analysis was conducted utilizing the "meta" package in R software, with the included studies assessed based on the Newcastle-Ottawa scale. RESULTS: A total of twelve studies were included and data from over 1,700 patients was collected. Patients with higher levels of Vd/Vt were more likely to not survive with an OR=1.27 [95% CI (1.09, 1.48), I2=93%, p<0.01]. In addition, non-survivors of ARDS had higher mean value levels of Vd/Vt than survivors with an MD=0.07 [95% CI (0.02, 0.11), I2=82%, p<0.01]. Furthermore, a leave-one-out meta-analysis was performed in order to assess the effect of each individual study on the overall outcome, which led to the lowering of heterogeneity to 0. CONCLUSIONS: The Vd/Vt ratio is an accurate index for determining the mortality of ARDS, reflecting the severity of the disease.
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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.047 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".