Awake tracheal intubation: A survey of practices, barriers and skills maintenance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it