Bibliographic record
Abstract
Difficult airway management problems are encountered more commonly during anaesthesia for ear, nose and throat (ENT) and maxillofacial procedures than probably any other branch of surgery. Surgery varies from high volume cases such as tonsillectomy and dento-alveolar surgery to complex head and neck cancer and craniofacial surgery. For a successful outcome these shared airway procedures require thorough pre-operative evaluation and close co-operation between anaesthetist and surgeon with an understanding of each other's problems and knowledge of specialist equipment. General ENT and maxillofacial procedures Airway management for ENT and maxillofacial procedures needs to take account of specific problems and a number of techniques may be useful (Table 24.1): Once surgery has commenced the anaesthetist is remote from the airway making adjustments to the airway more difficult and disruptive. Head and neck positioning for surgery and movements during surgery require a secure airway. For nasal and intraoral surgery the airway requires protection by soiling from blood and debris. Oropharyngeal and nasopharyngeal packs should be specifically recorded and accounted for at the end of the procedure. Direct inspection and suction clearance of blood and debris from the oro–nasopharynx should be undertaken at the end of the procedure. Face mask Until the introduction of the laryngeal mask airway (LMA) many simple ear procedures were undertaken with a face mask. The increased use of the LMA, compared to a face mask, for simple ear procedures is due to its ability to provide a clearer airway with a more effective airtight seal allowing better ventilation and monitoring of tidal gases. The anaesthetist's hands are also freed and factors which make facemask anaesthesia difficult, for example the edentulous bearded patient, have little impact on the quality of the airway with an LMA.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.122 | 0.072 |
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".