Endo-anesthesia: a primer
Bibliographic record
Abstract
Gastrointestinal (GI) endoscopy has witnessed a Cambrian explosion of techniques, indications, and expanding target populations. GI endoscopy encompasses traditional domains that include preventive measures, palliation, as alternative therapies in patients with prohibitive risks of more invasive procedures, and indicated primary treatments. But, it has expanded to include therapeutic and diagnostic interventional endosonography, luminal endoscopic resection, third space endotherapy, endohepatology, and endobariatrics. The lines between surgery and endoscopy are blurred on many occasions within this paradigm. Moreover, patients with high degrees of co-morbidity and complex physiology require more nuanced peri-endoscopic management. The rising demand for endoscopy services has resulted in the development of endoscopy referral centers that offer these invasive procedures as directly booked referrals for regional and rural patients. This further necessitates specialized programs to ensure appropriate evaluation, risk stratification, and optimization for safe sedation and general anesthesia if needed. This landscape is conducive to the organic evolution of endo-anesthesia to meet the needs of these focused and evolving practices. In this primer, we delineate important aspects of endo-anesthesia care and provide relevant clinical and logistical considerations pertaining to the breadth of procedures.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".