Herbal medications for surgical patients: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Postoperative nausea and vomiting (PONV) affect approximately 80% of surgical patients and is associated with increased length of hospital stay and systemic costs. Preoperative and postoperative pain, anxiety and depression are also commonly reported. Recent evidence regarding their safety and effectiveness has not been synthesised. The aim of this systematic review is to evaluate the efficacy and safety of herbal medications for the treatment and prevention of anxiety, depression, pain and PONV in patients undergoing laparoscopic, obstetrical/gynaecological and cardiovascular surgical procedures. METHODS AND ANALYSIS: The following electronic databases will be searched up to 1 October 2016 without language or publication status restrictions: CENTRAL, MEDLINE, EMBASE, CINAHL, Web of Science and LILACS. Randomised clinical trials enrolling adult surgical patients undergoing laparoscopic, obstetrical/gynaecological and cardiovascular surgeries and managed with herbal medication versus a control group (placebo, no intervention or active control) prophylactically or therapeutically will be considered eligible. Outcomes of interest will include the following: anxiety, depression, pain, nausea and vomiting. A team of reviewers will complete title and abstract screening and full-text screening for identified hits independently and in duplicate. Data extraction, risk of bias assessments and evaluation of the overall quality of evidence for each relevant outcome reported will be conducted independently and in duplicate using the Grading of Recommendations Assessment Development and Evaluation classification system. Dichotomous data will be summarised as risk ratios; continuous data will be summarised as standard average differences with 95% CIs. ETHICS AND DISSEMINATION: This is one of the first efforts to systematically summarise existing evidence evaluating the use of herbal medications in laparoscopic, obstetrical/gynaecological and cardiovascular surgical patients. The findings of this review will be disseminated through peer-reviewed publications and conference presentations. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42016042838.
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 | 0.057 | 0.054 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.020 | 0.013 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.092 | 0.011 |
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".