Choice of anaesthesia for category‐1 caesarean section in women with anticipated difficult tracheal intubation: the use of decision analysis
Bibliographic record
Abstract
A predicted difficult airway is sometimes considered a contra-indication to rapid sequence induction of general anaesthesia, even in an urgent case such as a category-1 caesarean section for fetal distress. However, formally assessing the risk is difficult because of the rarity and urgency of such cases. We have used decision analysis to quantify the time taken to establish anaesthesia, and probability of failure, of three possible anaesthetic methods, based on a systematic review of the literature. We considered rapid sequence induction of general anaesthesia with videolaryngoscopy, awake fibreoptic intubation and rapid spinal anaesthesia. Our results show a shorter mean (95% CI) time to induction of 100 (87-114) s using rapid sequence induction compared with 9 (7-11) min for awake fibreoptic intubation (p < 0.0001) and 6.3 (5.4-7.2) min for spinal anaesthesia (p < 0.0001). We calculate the risk of ultimate failed airway control after rapid sequence induction to be 21 (0-53) per 100,000 cases, and postulate that some mothers may accept such a risk in order to reduce potential fetal harm from an extended time interval until delivery. Although rapid sequence induction may not be the anaesthetic technique of choice for all cases in the circumstance of a category-1 caesarean section for fetal distress with a predicted difficult airway, we suggest that it is an acceptable option.
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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 | 0.037 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".