Tracheal Intubation in the Critically Ill. Where We Came from and Where We Should Go
Bibliographic record
Abstract
Abstract Tracheal intubation is commonly performed in critically ill patients. Unfortunately, this procedure also carries a high risk of complications; half of critically ill patients with difficult airways experience life-threatening complications. The high complication rates stem from difficulty with laryngoscopy and tube placement, consequences of physiologic derangement, and human factors, including failure to recognize and reluctance to manage the failed airway. The last 10 years have seen a rapid expansion in devices available that help overcome anatomic difficulties with laryngoscopy and provide rescue oxygenation in the setting of failed attempts. Recent research in critically ill patients has highlighted other important considerations for critically ill patients and evaluated interventions to reduce the risks with repeated attempts, desaturation, and cardiovascular collapse during emergency airway management. There are three actions that should be implemented to reduce the risk of danger: 1) preintubation assessment for potential difficulty (e.g., MACOCHA score); 2) preparation and optimization of the patient and team for difficulty—including using a checklist, acquiring necessary equipment, maximizing preoxygenation, and hemodynamic optimization; and 3) recognition and management of failure to restore oxygenation and reduce the risk of cardiopulmonary arrest. This review describes the history of emergency airway management and explores the challenges with modern emergency airway management in critically ill patients. We offer clinically relevant recommendations on the basis of current evidence, guidelines, and expert opinion.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".