Drug administration errors by South African anaesthetists--a survey.
Bibliographic record
Abstract
OBJECTIVES: To investigate the incidence, nature of and factors contributing towards wrong drug administrations by South African anaesthetists. DESIGN: A confidential, self-reporting survey was sent out to the 720 anaesthetists on the database of the South African Society of Anaesthesiologists. RESULTS: A total of 133 questionnaires were returned for analysis (18.5% response rate). Of the respondents, 125 (94%) admitted to having inadvertently administered a wrong drug. Thirty respondents (22.6%) said they had made errors on at least four occasions. A total of 303 specific wrong drug administrations were described. Nearly 50% involved muscle relaxants. A further 43 incidents (14%) involved the erroneous administration of vasoactive drugs. Five deaths and 3 nonfatal cardiac arrests were reported. In 9.9% of incidents the anaesthetic time was prolonged by more than 30 minutes. Contributory causes identified included syringe swaps (40%), misidentification of drugs (27.1%), fatigue (14.1%), distractions (4.7%), and mislabelling of syringes (4.7%). Only 19% of respondents regularly use colour-coded syringe labels complying with the national standard. CONCLUSIONS: Most anaesthetists experienced at least one drug error. The incidence of wrong drug administrations by South African anaesthetists appears to be similar to that in Australasia and Canada. The commonest error was a 'syringe swap' involving muscle relaxants. Most drug errors are inconsequential. An important minority of incidents result in severe morbidity or death. The study supports efforts to improve ampoule labelling, to encourage the use of syringe labels based on the international colour code and to develop a national reporting system for such incidents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".