Perspectives on ambulatory anesthesia: the patient’s point of view
Bibliographic record
Abstract
Herman Sehmbi, Jean Wong, David T WongDepartment of Anesthesia, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, ON, CanadaAbstract: Recent advances in anesthetic and surgical techniques have led to tremendous growth of ambulatory surgery. With patients with many co-morbid conditions undergoing complex procedures in an ambulatory setting, the challenges in providing ambulatory surgery and anesthesia are immense. In recent years, the paradigm has shifted from a health-care provider focus involving process compliance and clinical outcomes, to a patient-centered strategy that includes patients’ perspectives of desired outcomes. Improving preoperative patient education while reducing unnecessary testing, improving postoperative pain management, and reducing postoperative nausea and vomiting may help enhance patient satisfaction. The functional status of most patients is reduced postoperatively, and thus the pattern of recovery is an area of ongoing research. Standardized and validated psychometric questionnaires such as Quality of Recovery-40 and Postoperative Quality of Recovery Scale are potential tools to assess this. Patient satisfaction has been identified as an important outcome measure and dedicated tools to assess this in various clinical settings are needed. Identification of key aspects of ambulatory surgery deemed important from patients’ perspectives, and implementation of validated outcome questionnaires, are important in improving patient centered care and patient satisfaction.Keywords: ambulatory, patient, satisfaction, anesthesia, outcomes, questionnaire, perspectives
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".