Bibliographic record
Abstract
PURPOSE OF REVIEW: Preoperative anesthesia consultation before major surgery presents opportunities to better document comorbid illness, optimize medical conditions, facilitate referrals to specialists, order specialized investigations, initiate interventions to decrease risk, discuss aspects of perioperative care, and arrange appropriate postoperative care. The goal of this review is to discuss the implications of recent studies that have evaluated the processes-of-care and outcomes related to preoperative anesthesia consultation. RECENT FINDINGS: An increasing proportion of surgical patients undergo outpatient preoperative anesthesia consultation. These consultations effectively communicate information to anesthesia providers in operating rooms, reduce the time required to complete preoperative assessments, improve patients' education about perioperative care, and increase patient acceptance of regional anesthesia. Recent population-based data also demonstrate that consultations are associated with reductions in hospital length-of-stay, but not postoperative mortality. In addition, rates of specialized preoperative cardiac testing are increased following anesthesia consultation but the value of these tests remains debatable. SUMMARY: Preoperative anesthesia consultations have become increasingly common and have shown some clear beneficial effects on perioperative care and outcomes. Further research remains needed to identify efficacious interventions for reducing perioperative risk, measure the prognostic value of specialized preoperative tests, and compare the safety of different models for performing preoperative consultations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".