Bibliographic record
Abstract
Recording details of airway management on anaesthetic charts informs future safe management of patients undergoing anaesthesia, particularly if patients have a difficult or potentially difficult airway 1. The range of airway equipment available to anaesthetists for managing difficult airways has changed significantly in the last decade. ‘Difficult’ or ‘easy’ no longer define airways that can be intubated using a Macintosh laryngoscope. In a recent survey of Canadian anaesthetists, for example, over 90% choose to use a videolaryngoscope in situations of ‘cannot intubate and cannot ventilate’ 2. However, we suggest that documentation by anaesthetists has not kept pace with advances in what equipment or techniques they used for difficult airway management, and that this leads to potentially avoidable airway problems during subsequent anaesthesia. We would like to invite the Difficult Airway Society to comment on whether they have considered modifying their airway alert document 3, or recommending a standardised anaesthetic chart section for airway management to this end, by including specific details of equipment used and sequential management?
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 | 0.018 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".