Lessons to learn from epidemiologic studies in ARDS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it