Important Findings from an In-depth Analysis of a Medication Incident
Bibliographic record
Abstract
INTRODUCTION In May 2007, the Alberta Cancer Board released the document Fluorouracil Incident Root Cause Analysis1 for shared learning. The incident under analysis involved administration of a high dose of fluorouracil (4000 mg/m2; total dose 5250 mg) over 4 h instead of the intended 4 days. The protocol also included administration of a single dose of 100 mg cisplatin. The patient, a 43-year-old woman with advanced nasopharyngeal carcinoma, died 22 days later of the sequelae of fluorouracil toxicity, cumulative with cisplatin toxicity. The Institute for Safe Medication Practices Canada (ISMP Canada) was invited to provide external expertise for the root cause analysis of this event. Providing such assistance is one of ISMP Canada’s defined roles in the Canadian Medication Incident Reporting and Prevention System. The recommendations in the report1 were directed specifically toward safer management of high-dose fluorouracil protocols and may be relevant to the management of other chemotherapy agents and other high-alert medications. One of the recommendations was to disseminate widely the findings of the root cause analysis as a way to enhance awareness of the hazards identified. This article presents selected findings and excerpts from the report that are highly relevant to pharmacists. Root cause analysis is a structured process for a comprehensive system-based review of critical incidents to determine what happened, why it happened, and what can be done to reduce the likelihood of recurrence.2 Root cause analysis of a medication incident identifies hazards, issues, contributing factors, and underlying causes. This information is used to develop safeguards to prevent similar adverse events or to mitigate harm to patients if an incident does occur again.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".