Awake tracheal intubation: A survey of practices, barriers and skills maintenance
Bibliographic record
Abstract
Awake tracheal intubation (ATI) is advocated in situations where complex airway anatomy or deranged physiology make usual post-induction airway management hazardous. The safety of ATI has been described in many settings. Nevertheless, it is not always performed when indicated, and significant patient harm as a consequence is still reported. A survey was conducted to investigate anaesthetists' practices and possible reasons for reticence in performing ATI. The survey also sought to explore solutions to limited opportunities for training and skills maintenance. The 17-question survey was sent to a random selection of 1400 consultant anaesthetists across Australia and New Zealand in 2023. The response rate was 36% (499 of 1400). Forty percent (198 of 499) (95% confidence interval (CI) 35 to 44) of participants had not performed an ATI in the last 12 months. The majority of participants (64% (317 of 499) (95% CI 59 to 68)) agreed that there were barriers in their own practice to performing ATI. There was strong agreement that proficiency in ATI should be within the skillset of on-call anaesthetists (81% (400 of 494) (95% CI 78 to 84)). There was also strong support for ATI to become a mandatory core skill (74% (368 of 497) (95% CI 70 to 78) of participants). Current volume of practice for trainees was almost universally considered insufficient (93% (459 of 496) (95% CI 90 to 95)). There is a disparity between the perceived importance of competence in ATI and the limited volume of practice expected of trainees and paucity of ongoing clinical exposure for consultants. Training and programs to maintain skills in ATI are urgently required to address this.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".