Continued under-recognition of acute respiratory distress syndrome after the Berlin definition: what is the solution?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Timely recognition of acute respiratory distress syndrome (ARDS) may allow for more prompt management and less exacerbation of lung injury. However, the absence of a diagnostic test for ARDS means that the diagnosis of ARDS requires clinician recognition in what is usually a complicated and evolving illness. We review data concerning the extent of recognition of ARDS in the era of the Berlin definition of ARDS. RECENT FINDINGS: ARDS continues to be under-recognized - even in the era of the more recent 'Berlin' definition, and significant delay in its recognition is common. Factors contributing to under-recognition may include the complexity of ARDS biology, low specificity of the consensus (diagnostic) criteria, and concerns about reliable interpretation of the chest radiograph. Understandably, 'external' factors are also at play: ICU occupancy and higher patient to clinician ratio impair recognition of ARDS. Timely recognition of ARDS appears important, as it is associated with the use of higher PEEP, prone positioning and neuromuscular blockade which can lower mortality. Computer-aided decision tools seem diagnostically useful, and together with the integration of reliable biomarkers, may further enhance and speed recognition of this syndrome. SUMMARY: Significant numbers of patients with ARDS are still unrecognized by clinicians in the era of the Berlin definition of ARDS, with potentially important consequences for patient management and outcome.
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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 | 0.001 | 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 | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".