An Analysis of Substandard Propofol Detected in Use in Zambian Anesthesia
Bibliographic record
Abstract
BACKGROUND: In early 2015, clinicians throughout Zambia noted a range of unpredictable adverse events after the administration of propofol, including urticaria, bronchospasm, profound hypotension, and most predictably an inadequate depth of anesthesia. Suspecting that the propofol itself may have been substandard, samples were procured and sent for testing. METHODS: Three vials from 2 different batches were analyzed using gas chromatography-mass spectrometry methods at the John L. Holmes Mass Spectrometry Facility. RESULTS: Laboratory gas chromatography-mass spectrometry analysis determined that, although all vials contained propofol, its concentration differed between samples and in all cases was well below the stated quantity. Two vials from 1 batch contained only 44% ± 11% and 54% ± 12% of the stated quantity, whereas the third vial from a second batch contained only 57% ± 9%. The analysis found that there were no hexane-soluble impurities in the samples. CONCLUSIONS: None of the analyzed vials contained the stated amount of propofol; however, our analysis did not detect additional contaminants that would explain the adverse events reported by clinicians. Our results confirm the presence of substandard propofol in Zambia; however, anecdotal accounts of substandard anesthetic medicines in other countries abound and warrant further investigation to provide estimates of the prevalence and scope of this global problem.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".