Patient characteristics, management and outcomes in a Nordic subset of the “large observational study to understand the global impact of severe acute respiratory failure” (<scp>LUNG SAFE</scp>) study
Bibliographic record
Abstract
BACKGROUND: The "Large observational study to understand the global impact of severe acute respiratory failure" (LUNG SAFE) study described the worldwide epidemiology and management of patients with acute hypoxaemic respiratory failure (AHRF). Here, we present the Nordic subset of data from the LUNG SAFE cohort. METHODS: We extracted LUNG SAFE data for adults fulfilling criteria for AHRF in intensive care units (ICU) in Denmark, Norway and Sweden, including demographics, co-morbidities, clinical assessment and management characteristics, 90-day survival and length-of-stay (LOS). We analysed ICU LOS with linear regression, and associations between risk factors and mortality were quantified using Cox regression. RESULTS: We included 192 patients, with a median age of 64 years (IQR 55, 72), and a male-to-female ratio of 2:1. The majority had one or more co-morbidities, and clinicians identified pneumonia as the primary cause of respiratory failure in 56% and acute respiratory distress syndrome (ARDS) in 21%. Median ICU LOS and duration of invasive mechanical ventilation (IMV) were 5 and 3 days. Tidal volumes (TV) were frequently larger than that supported by evidence and IMV allowing for spontaneous ventilation was common. Younger age, co-morbidity, surgical admission and ARDS were associated with ICU LOS. Sixty-one patients (32%) were dead at 90 days. Age and a non-surgical cause of admission were associated with death. CONCLUSIONS: In this subset of LUNG SAFE, ARDS was often not recognised in patients with AHRF and management frequently deviated from evidence-based practices. ICU LOS was generally short, and mortality was attributable to known risk factors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".