Current knowledge and perspectives in respiratory management for immunocompromised patients with acute respiratory failure
Bibliographic record
Abstract
PURPOSE: One of the most common causes of intensive care unit admission in the immunocompromised population is acute respiratory failure. This population has many unique characteristics that render their respiratory failure risk factors, etiologies, and management different from the general nonimmunocompromised population. While mortality rates have improved in the setting of invasive mechanical ventilation, it remains higher than the general population, making prevention of intubation a key area of interest. RECENT FINDINGS: Acute respiratory failure in immunocompromised patients is common, complex, and associated with a high case-fatality rate. Ventilatory strategies should be tailored to the clinical context and to the prognosis of the underlying condition. In eligible patients, early ICU admission, a thorough work up to identify the etiology and invasive mechanical ventilation should not be delayed once criteria for intubation are met, despite attempts at noninvasive oxygenation. Future research should aim move beyond a binary definition of immunosuppression and account for its complexities to identify sub-phenotypes most likely to benefit from specific therapeutic strategies, thereby advancing the personalization of care. SUMMARY: This review explores the literature on noninvasive respiratory support, invasive mechanical ventilation, and extracorporeal life support and the unique considerations in the immunocompromised population.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".