Clinical Issues and Research in Respiratory Failure from Severe Acute Respiratory Syndrome
Bibliographic record
Abstract
The National Heart, Lung, and Blood Institute, along with the Centers for Disease Control and Prevention and the National Institute of Allergy and Infectious Diseases, convened a panel to develop recommendations for treatment, prevention, and research for respiratory failure from severe acute respiratory syndrome (SARS) and other newly emerging infections. The clinical and pathological features of acute lung injury (ALI) from SARS appear indistinguishable from ALI from other causes. The mainstay of treatments for ALI remains supportive. Patients with ALI from SARS who require mechanical ventilation should receive a lung protective, low tidal volume strategy. Adjuvant treatments recommended include prevention of venous thromboembolism, stress ulcer prophylaxis, and semirecumbent positioning during ventilation. Based on previous experience in Canada, infection control resources and protocols were recommended. Leadership structure, communication, training, and morale are an essential aspect of SARS management. A multicenter, placebo-controlled trial of corticosteroids for late SARS is justified because of widespread clinical use and uncertainties about relative risks and benefits. Studies of combined pathophysiologic endpoints were recommended, with mortality as a secondary endpoint. The group recommended preparation for studies, including protocols, ethical considerations, Web-based registries, and data entry systems.
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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 | 0.179 | 0.293 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.019 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".