Intubation outside of the operating room: new challenges and opportunities in COVID-19 era
Bibliographic record
Abstract
PURPOSE OF REVIEW: Airway management in patients outside the operating room is associated with increased difficulties and risks, and the setting of the COVID-19 global pandemic adds another layer of complexity. Therefore, endotracheal intubation (ETT) of a patient who is presumptive COVID-19 or COVID-19 positive presents an additional challenge to an anesthesiologist. The aim of this review is to summarize the important principles of airway management outside of the operating room during the COVID-19 pandemic. RECENT FINDINGS: Several professional societies have formulated guidelines on airway management COVID-19 suspect and proven patients. Additionally, anesthesiologists working in hospitals treating many infected patients have developed specialized teams responsible for airway management outside the operating room. These documents and protocols focus on the importance of wearing personal protective equipment and the skills of the providers responsible for securing the airway. Staff safety is always a priority when performing ETT outside operating room. SUMMARY: The COVID-19 pandemic redefined the management of patients requiring aerosol generating procedures (droplet and airborne precautions). ETT is one of them and anesthesiologists are experts in performing airway management. Although the operating room is a highly controlled environment, airway management outside of this setting is not always the easiest task.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".