Anaesthesiologists' views on the need for point-of-care information system in the operating room: a survey of the European Society of Anaesthesiologists
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: In this preliminary study we wanted to explore the attitudes of anaesthesiologists to a point-of-care information system in the operating room. The study was conducted as a preliminary step in the process of developing such a system by the European Society of Anaesthesiologists (ESA). METHODS: A questionnaire was distributed to all 2240 attendees of the ESA's annual meeting in Gothenburg, Sweden, which took place in April 2001. RESULTS: Of the 329 responders (response rate of 14.6%), 79% were qualified specialists with more than 10 yr of experience (68%), mostly from Western Europe. Most responders admitted to regularly experiencing lack of medical knowledge relating to real-time patient care at least once a month (74%) or at least once a week (46%), and 39% admitted to having made errors during anaesthesia due to lack of medical information that can be otherwise found in a handbook. The choice ofa less optimal but more familiar approach to patient management due to lack of knowledge was reported by 37%. Eighty-eight percent of responders believe that having a point-of-care information system for the anaesthesiologists in the operating room is either important or very important. CONCLUSIONS: This preliminary survey demonstrates that lack of knowledge of anaesthesiologists may be a significant source of medical errors in the operating room, and suggests that a point-of-care information system for the anaesthesiologist may be of value.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".