Bibliographic record
Abstract
PURPOSE OF REVIEW: To summarize and examine the updated published results on the outcome measures that can be used to assess the quality of ambulatory surgery and anesthesia. RECENT FINDINGS: Major morbidity and mortality following ambulatory surgery is exceedingly low. Cancellations and delays may have a negative impact on the patients, healthcare personnel and the organizations. Minor cardiovascular adverse events are the most common intraoperatively and are associated with preexisting cardiovascular diseases and elderly patients. Respiratory events postoperatively are associated with obesity, smoking and asthma. Also, pain is a common cause for longer postoperative stay, unanticipated admission and readmission. Postoperative nausea and vomiting occurs in 30% of patients and strongly affects patient satisfaction. Furthermore, prolonged stays are mainly caused by surgical factors, or minor symptoms like pain or nausea. Surgical factors are also the main causes of unanticipated hospital admission. The type of surgery and the 24 h postoperative symptoms may affect the degree of return to daily living function. Also, patient satisfaction affects the outcome of healthcare and the use of healthcare services. SUMMARY: Ambulatory surgery, as currently practiced, provides quality care that is cost-effective. Minor adverse events such as pain and postoperative nausea and vomiting are still common, and improvement could be targeted in these areas.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| 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) | 0.006 | 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".