An evidence‐based approach to airway management: is there a role for clinical practice guidelines?
Bibliographic record
Abstract
Complications arising out of airway management represent an important cause of anaesthesia-associated morbidity and mortality. Anaesthetic practice itself can lead to preventable harm, a particular example being persistent attempts at direct laryngoscopy, that results in delay in employing alternative strategies (or devices) when intubation is difficult. When patients are injured, expert review is called upon and often concludes that airway management provided by the anaesthetists was substandard. Many training programmes do not offer their trainees structured or organised teaching in airway management and many trainees probably enter practice with limited skills to deal with difficult airways. The literature on the management of the difficult airway in anaesthesia practice (especially as it relates to new technology and salvage strategies) is expanding rapidly. New technologies and practised response algorithms may be helpful in the management of the difficult airway, reducing the potential for adverse patient outcomes. Specialist societies and national interest groups can play an important role by critically reviewing and then applying the evidence base to generate clinical practice guidelines. The recommendations contained in such guidelines should be based on the most current evidence and they should be reviewed regularly for their content and continued relevance.
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 | 0.090 | 0.271 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".