GOLDEN anniversary of the acute respiratory distress syndrome: still much work to do!
Bibliographic record
Abstract
PURPOSE OF REVIEW: Over the past 50 years, we have developed a conceptual model of the acute respiratory distress syndrome (ARDS), and have witnessed significant advances in the care of patients with ARDS. In this commentary, we will discuss recent published articles reporting or suggesting new conceptual models for diagnosis, classification, stratification, prevention, ventilatory management, pharmacologic treatment, and outcome prediction of ARDS. RECENT FINDINGS: This review is a tribute to all clinicians and investigators that have contributed to a better understanding of ARDS. However, we will mainly focus on publications from the last 24 months dealing with clinically relevant issues, and we briefly mention recent approaches in respiratory intensive care medicine that could improve ARDS outcome. SUMMARY: Because this year is the golden anniversary of the first clinical description of ARDS in 1967, we have used the acronym 'G.O.L.D.E.N.' to frame our review. It is clear that although much has been learned over the past 50 years, there is still much work to do to unravel and control this continuously surprising syndrome.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".