Development of a perioperative medicine research agenda: a cross sectional survey
Bibliographic record
Abstract
BACKGROUND: Post-operative complications are a significant source of morbidity and mortality for patients undergoing surgery. However, there is little research in the emerging field of perioperative medicine beyond cardiac risk stratification. We sought to determine the research priorities for perioperative medicine using a cross sectional survey of Canadian and American general internists. METHODS: Surveys were electronically sent to 312 general internists from the Canadian Society of Internal Medicine and 130 internists from the perioperative medicine research interest group within the US based Society of General Internal Medicine. The questionnaire contained thirty research questions and respondents were asked to rate the priority of these questions for future study. RESULTS: The research topics with the highest ratings included: the need for tight control of diabetes mellitus postoperatively and the value of starting aspirin on patients at increased risk for postoperative cardiac events. Research questions evaluating the efficacy and safety of perioperative interventions had higher ratings than questions relating to the prediction of postoperative risk. Questions relating to the yield of preoperative diagnostic tests had the lowest ratings (p < 0.001 for differences across these categories). CONCLUSION: The results of this survey suggest that practicing general internists believe that interventions studies are a priority within perioperative medicine. These findings should help prioritize research in this emerging field.
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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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".