Rethinking Acute Respiratory Distress Syndrome after COVID-19: If a “Better” Definition Is the Answer, What Is the Question?
Bibliographic record
Abstract
The definition of acute respiratory distress syndrome (ARDS) has a somewhat controversial history, with some even questioning the need for the term "ARDS." This controversy has been amplified by the coronavirus disease (COVID-19) pandemic given the marked increase in the incidence of ARDS, the relatively new treatment modalities that do not fit neatly with the Berlin definition, and the difficulty of making the diagnosis in resource-limited settings. We propose that attempts to revise the definition of ARDS should apply the framework originally developed by psychologists and social scientists and used by other medical disciplines to generate and assess definitions of clinical syndromes that do not have gold standards. This framework is structured around measures of reliability, feasibility, and validity. Future revisions of the definition of ARDS should contain the purpose, the methodology, and the framework for empirically testing any proposed definition. Attempts to revise critical illness syndromes' definitions usually hope to make them "better"; our recommendation is that future attempts use the same criteria used by other fields in defining what "better" means.
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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 | 0.066 | 0.125 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.010 | 0.030 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.036 |
| Insufficient payload (model declined to judge) | 0.002 | 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".