Preoperative risk assessment and optimization integrating surgical and anesthetic principles and practices: a national survey for internists
Bibliographic record
Abstract
BACKGROUND: The integration of procedure-specific risks into preoperative patient assessment and optimization are crucial aspects of perioperative care. However, data on internists' knowledge of surgical and anesthetic principles and practices are limited. We thus sought to identify internists' knowledge gaps in terms of surgical- and anesthetic-specific risk factors and characteristics. METHODS: An open and voluntary e-survey was conducted via LimeSurvey between April and July 2021 to evaluate Canadian internists' knowledge of surgical and anesthetic principles and practices. The survey included the perceived importance and knowledge of several key surgical and anesthetic aspects, such as surgery duration, procedure-specific cardiac risk, bleeding risk, and thrombotic risk. It also assessed pre- and post-survey self-reported confidence levels in one's knowledge of these characteristics. Finally, we investigated how internists optimize some of the preoperative risks. RESULTS: A total of 173 Canadian internists opened the survey link, and 121 completed it (completion rate 70%). While the majority of respondents considered surgical and anesthetic principles and practices as important, most identified knowledge gaps. Participants generally estimated surgery duration and procedure-specific cardiac risk adequately. However, they tended to underestimate procedure-specific bleeding risk for general (58%) and orthopedic (76%) surgeries and to overestimate procedure-specific thrombotic risk for vascular (63%) and genitourinary (60%) surgeries. Furthermore, there is a lack of consensus regarding the appropriate hemoglobin A1c target and 0% of respondents reported using the guideline-suggested hemoglobin threshold for investigation and intervention. CONCLUSIONS: Overall, our findings identify significant knowledge gaps among Canadian internists in preoperative assessment of procedure-specific risk factors and can be used to inform both the development of educational initiatives and future research to improve the quality of preoperative patient care.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".