Current incidence and outcome of the acute respiratory distress syndrome
Bibliographic record
Abstract
PURPOSE OF REVIEW: This article discusses recently published articles reporting the incidence and outcome of patients with the acute respiratory distress syndrome (ARDS). This is a difficult task since there is a marked variability regarding the methodology of the few, large epidemiological, and observational studies on ARDS. RECENT FINDINGS: The review will mainly focus on publications from the past 18 months. We have reviewed new epidemiological studies reporting population-based incidence of ARDS. Also, we have reviewed the data on survival reported in observational and randomized controlled trials, discussed how the current ARDS definition modifies the true incidence of ARDS, and briefly mentioned recent approaches that appear to improve ARDS outcome. SUMMARY: On the basis of current evidence, it seems that the incidence and overall hospital mortality of ARDS has not changed substantially in the last decade. Independent of the definition used for identification of ARDS patients, reported population-based incidence of ARDS is an order of magnitude lower in Europe than in the USA. Current hospital mortality of combined moderate and severe ARDS reported in observational studies is greater than 40%.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".