Bibliographic record
Abstract
Every anaesthetist will reach the end of his/her career with a collection of difficult airway experiences.There can be few more terrifying experiences in medical practice than the realization that a cyanosed patient is getting worse, not better, particularly when the patient was nice and pink before the anaesthetic began. Many seasoned anaesthetists recognize the change that comes over trainees after their first experience of serious difficulty with the airway. One of the editors remembers a conversation with a distinguished American paediatric anaesthetist about the difficulty of keeping up with bright young residents. Her observation that ‘ a few deep paediatric desaturations sure does take the shine off'em ’ was correct. All of us who have been around for some time have ‘been there’, and we know we could find ourselves in difficulty any time we give an anaesthetic. Management of the airway of patients who are sedated, obtunded or anaesthetized is the responsibility of nursing and medical practitioners in anaesthesia, emergency medicine, intensive care medicine and other critical care areas. Anaesthetists do not own the airway and their right to be considered expert can be based only on good clinical practice, knowledge of relevant basic science and critical evaluation of every component of airway care. There is an uneasy combination of science and art in airway management. We know a good deal about the physics and physiology, but are less certain about the safest way to manage the airway in many patients. Meetings of the Difficult Airway Society have often been lacking in consensus, sometimes confusing, but always educational. It is probably just that there really are several ways to pluck a chicken.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.398 | 0.221 |
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".