10.1016/b978-0-323-42881-1.00051-1
Bibliographic record
Abstract
The communication of information concerning patients with difficult airways is universally recognized as an important component in avoiding future airway management difficulties. A range of options is available to impart this information; little is known, however, about the referral patterns of anaesthetists following the identification and management of a difficult airway. In this study, 158 anaesthetists were contacted and asked to comment on their referral patterns regarding a number of difficult airway scenarios. This was followed by a retrospective survey of 124 patients with known difficult airways. A wide discrepancy was found between stated referral preferences by anaesthetists, and the actual use of options such as postoperative visits, notes in the clinical record, letters to the patient and family doctor, and entries in hospital, national and MedicAlert™ data bases. Of the patients with an airway difficulty noted on their anaesthetic record, only 14% of them also had a pertinent comment on their clinical record; even fewer were referred to hospital warning systems (12%) or national (6%) and MedicAlert™ (7%) databases.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.980 | 0.988 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".