Bibliographic record
Abstract
Acute respiratory distress syndrome (ARDS) is a common, lethal, and morbid respiratory complication primarily seen in the setting of major trauma and infection. Despite advances in mechanical ventilation for ARDS, many interventions have not been successful in reducing mortality. Recent grant announcements and ongoing clinical trials indicate an interest in preventing ARDS. This Perspective challenges some of the basic assumptions of ARDS prevention and preventive care in the intensive care unit. ARDS is an organ function surrogate outcome. Studies of surrogate outcomes in medicine have repeatedly failed to show an association with patient-centered outcomes that might include mortality, quality of life, patient satisfaction, and cost. Organ failure surrogate outcomes in critical care, including oxygenation, cardiac output, and blood pressure, have similarly failed to show a consistent association with patient-centered benefit. Trials designed to demonstrate an effect on surrogate outcomes will rarely be able to demonstrate small, but important, harms so that the net benefit of prevention can be calculated. This will leave clinicians with insufficient information to balance the unknown benefits of ARDS prevention with imprecisely estimated costs or risks of prevention. Because ARDS diagnosis relies on oxygenation and the chest radiograph that might be directly influenced by the prophylactic intervention, studies must be designed to insure that the prevention is not merely cosmetic. Strategies that prevent ARDS need to be tested in trials sufficiently powered to demonstrate their patient-centered costs, benefits and harms before widespread adoption.
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 | 0.009 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 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".