Lessons to learn from epidemiologic studies in ARDS
Bibliographic record
Abstract
PURPOSE OF REVIEW: Recent advances in our understanding of the epidemiology of ARDS has generated key insights into the incidence, risk factors, demographics, management and outcomes from this devastating clinical syndrome. RECENT FINDINGS: ARDS occurs in 10% of all ICU patients, in 23% of all mechanically ventilated patients, with 5.5 cases per ICU bed each year. Although some regional variation exists regarding ARDS incidence, this may be less than previously thought. Subphenotypes are increasingly identified within the ARDS cohort, with studies identifying a 'hyperinflammatory' or 'reactive' subgroup that has a higher mortality, and may respond differently to therapeutic interventions. Demographic factors, such as race, may also affect the therapeutic response. Although mortality in ARDS is decreasing in clinical trials, it remains unchanged at approximately 40% in major observational studies. Modifiable ventilatory management factors, including PEEP, airway pressures, and respiratory rate are associated with mortality in ARDS. Hospital and ICU organizational factors play a role in outcome, whereas socioeconomic status is independently associated with survival in patients with ARDS. The Kigali adaptation of the Berlin ARDS definition may provide useful insights into the burden of ARDS in the developing world. SUMMARY: ARDS exerts a substantial disease burden, with 40% of patients dying in hospital. Diverse factors, including patient-related factors such as age and illness severity, country level socioeconomic status, and ventilator management and ICU organizational factors each contribute to outcome from ARDS. Addressing these issues provides opportunities to improve outcome in patients with ARDS.
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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 | 0.014 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".