Bibliographic record
Abstract
PURPOSE OF REVIEW: Anesthesia for ambulatory surgery has come a long way since 1842 when James Venable underwent surgery for removal of a neck mass with Crawford W. Long administering ether and also being the surgeon. We examine major advances over the past century and a half. RECENT FINDINGS: The development of anesthesia as a medical specialty is perhaps the single most important improvement that has enabled advances in the surgical specialties. Moreover, improved equipment, monitoring, training, evaluation of patients, discovery of better anesthetic agents, pain control, and the evolution of perioperative care are the main reasons why ambulatory anesthesia remains so safe in modern times. The development of less invasive surgical techniques, economic factors, and patient preferences provided addition impetus to the popularity of ambulatory surgery. SUMMARY: Beyond the discovery in the mid-19th century that ether and nitrous oxide could be used to render patients unconscious during surgical procedures, subsequent developments in our specialty have added modestly, in a stepwise manner, to reduce mortality and morbidity associated with its use. These improvements have allowed us to safely meet the steadily increasing demand for ambulatory surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".