A Survey of Medication Errors among Anaesthesiologists of Karnataka - A Pilot Study
Bibliographic record
Abstract
Background: Medication errors in modern medical practice add to the cost of patient care and increase human suffering. We decided to conduct a survey among practicing anesthesiologists of our region in order to assess the prevalence of medication errors, the current knowledge of medication errors, common drugs involved and the possible corrective measures. Methods: After obtaining an approval from the institutional ethical committee a pre- validated questionnaire of 18 questions was distributed by electronic mail to the members of Indian Society of Anaesthesiologists, Karnataka state branch. Results: We received 324 survey responses. The percentage of anaesthesiologists who attempted the survey was 14.37% (the number of people who attempted the survey divided by the number of emails delivered including those who opted out) Out of these, 242 respondents (74.69%) said that they had experienced or witnessed at least 1 medication error (Figure 1). Of the 142 respondents who answered the question, 79.58% had a near miss event, and 53.52% had not reported the incident to the hospital authorities (Figure 2). Majority of the incidents involved adults (86.2%), 59.8 % involved substitution of a drug, and the errors were more common with general anaesthesia (84.51%). Conclusion: Our survey showed that most respondents believed in a reporting system for medication errors and that multiple precautionary measures were required to prevent such events.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 |
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".