Should we induce general anesthesia in the prone position?
Bibliographic record
Abstract
PURPOSE OF REVIEW: For patients requiring surgery in the prone position, an alternative to a traditional supine induction is allowing the patient to position themselves comfortably prone and inducing anesthesia in that position. The purpose of this review is to examine the current literature and evaluate the safety of induction of anesthesia in the prone position. RECENT FINDINGS: The first randomized trial comparing induction in the supine vs. prone position for patients requiring spinal surgery was published earlier this year and reported a time-saving benefit. Multiple case series report the feasibility of this approach; however, the potential benefits of prone induction, namely a reduction in pressure injuries and avoidance of complications of the turn itself, remain unproven. Increased familiarity with prone insertion of supraglottic airways is a useful tool in case of accidental intraoperative extubation in a patient who is already prone. Potential disadvantages include loss of the airway during induction, reduced ability to manage adverse hemodynamic consequences of induction and restriction to use of a supraglottic airway. SUMMARY: The reviewed literature shows that elective prone induction of anesthesia using supraglottic airways, in select patients, is feasible and associated with very low complication rates; however, there is insufficient evidence to suggest that this should be done routinely.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".