Medication Safety in the Operating Room: Teaming Up to Improve Patient Safety
Bibliographic record
Abstract
S everal studies have suggested that medication error is a leading cause of adverse events during anesthesia.For example, in an analysis of critical events during anesthesia, Cooper et al. (1984) demonstrated that the total number of medication-related events (including syringe swaps, drug ampoule swaps, overdoses and incorrect drug choices) far exceeded the next most frequent problem, disconnection of the breathing circuit.In a large Australian survey, Webster et al. ( 2001) estimated the incidence of drug administration errors in anesthesia on the basis of a large, prospective set of data.Overall, one drug administration error was reported for every 133 anesthetics administered.A survey of 687 anesthesiologists (representing a 30% response rate) (Orser et al. 2001) revealed that 85% of the respondents had experienced at least one drug error or near miss.A variety of factors contribute to increases in the risk of medication error in patients undergoing anesthesia, including the use of potent drugs that carry a risk of serious injury or death when administered in excessive doses or without adequate patient support; the dynamic, complex environment of the operating suite; and the fact that one person is responsible for prescribing, dispensing and administering the anesthetic and Medication Safety in the Operating Room: Teaming Up to Improve Patient Safety
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".