SOGC CLINICAL PRACTICE GUIDELINE Guideline for the Management of Postoperative Nausea and Vomiting
Bibliographic record
Abstract
Objective: To provide recommendations for the management of postoperative nausea and vomiting (PONV), which may affect as many as 30 % of patients. Methods and Evidence: Medline, PubMed, and the Cochrane Database were searched for articles published in English from 1995 to 2007. Recognizing that we must work as a team to optimize the care of our patients perioperatively, this guideline was written in partnership with anaesthesiologists. Options: The areas of clinical practice considered in formulating this guideline are prevention and prophylaxis, treatment, both medical and alternative, and patient education. Outcomes: Implementation of this guideline should optimize the prevention of and prophylaxis against PONV and the prompt treatment of women who suffer from PONV following gynaecologic surgery. Increased awareness of options for management should help minimize the effects of PONV. Benefits, Harms, and Costs: PONV results not only in increased patient discomfort and dissatisfaction but also in increased costs related to length of hospital stay. Cost of medications to prevent and treat PONV must be weighed against improved surgical experience for the patient and decreased costs to the system. Values: Recommendations were made according to the guidelines developed by the Canadian Task Force on Preventive Health
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 | 0.009 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".